INDIAN SCHOOL SOHAR CCE ASSESSMENT CLASSES VI - VIII SESSION 2014-15
CCE -PROFICIENCE INDIAN INSTITUTE OF SCIENCE …
Transcript of CCE -PROFICIENCE INDIAN INSTITUTE OF SCIENCE …
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INFORMATION HANDBOOK
August -December 2021
CCE -PROFICIENCE
INDIAN INSTITUTE OF SCIENCE
BENGALURU-560012
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Particulars Page
No.
Introduction 3
Instructions 5
Course Schedule 7
Fee Structure 9
Performa for Certificate 36
REGULAR COURSES
Sl.
No. Name of the Course Credit
1 Innovation and Design (Mon) (6 pm to 8 pm) 2:0
2 Quantum Theory (Mon & Wed) (6 pm to 7.30 pm) 3:0
3 Soil Dynamics (Mon & Wed) (6 pm to 7.30 pm) 3:0
4 Applied Linear Algebra for Engineers and Researchers (Sun) (10 am to 12 pm) 2:0
5 Service Design Thinking (Tue) (6 pm – 8 pm) 2:0
6 Vaccine and Immunity (Tue & Thu) (6 pm to 7.30 pm) 3:0
7 Vibration and Noise: Theory and Practice (Wed) (6 pm – 8 pm) 2:0
8 Probability Foundations for Machine Learning (Fri) (6 pm – 8 pm) 2:0
9 Analysis and Design of Composite Structures (Fri) (6 pm – 8 pm) 2:0
10 One and Two-Dimensional NMR Spectroscopy for Chemists (Sat) (10 am to 1 pm) 3:0
11 Artificial Intelligence (Sat) (10 am to 1 pm) 3:0
12 Data Structures and Graph Analytics (Sat) (10 am – 12 pm) 2:0
13 Project Management (Sat) (10 am to 12 pm) 2:0
14 Introduction to Computing for AI & Machine Learning (Sat) (10 am to 1 pm) 3:0
15. Deep Learning (Sat) (10 am to 1 pm) 3:0
16. Start-up Tools (SuT) (Sat) (10 am to 1 pm) 3:0
17. Structural Analysis and Design Optimization: Theory and Practice (Sat) (12 pm – 2pm) 2:0
18. Foundations of Data Science and Machine Learning (Sat) (1 pm to 4 pm) 3:0
19. Business Analytics with Management Science Models and Methods (Sat) (10 am – 1
pm) 3:0
20. Reinforcement and Deep Reinforcement Learning (Sat) (1 pm – 4 pm) 3:0
21. Mathematics for Machine Learning (Sat) (2 pm – 5 pm) 3:0
22. Introduction to AI & Machine Learning (Sat) (1 pm – 4 pm) 3:0
23. Fundamentals of Machine Learning (Sat) (10 am – 1 pm) 3:0
24. Embedded Systems and IoT Sensors Applications (Sat) (2 pm to 4 pm) 2:0
25. Foundations of Intellectual Property Rights (IPRs) – Legal and policy perspective (Sat)
(2 pm – 4 pm) 2:0
26. Machine Learning (Sun) (10 am to 1 pm) 3:0
CONTENTS
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INTRODUCTION
Indian Institute of Science (IISc) established in 1909, is a Deemed University and Centrally Funder
Technical Institution under the Department of Higher Education, Ministry of Human Resources
Development, Government of India. Rapid strides in science and technology make it imperative
that the education of professionals be continued over their entire career rather than be confined to a
single stretch. What is needed is a complete integration of education with work during their
productive life span, which will be adequate to help them cope with new demands. Continuing
Education embraces all the processes of education that one undergoes throughout a working life
and which have a relevance to the practical problems likely to be encountered in one’s career. It
may be realized through formal and informal modes of teaching, or through mass media. In recent
years, there has been a growing awareness on the part of Universities that imparting knowledge to
people beyond their boundaries is an equally important part of their service to the community. With
this broad perspective of their function in society, Universities have begun to seek ways of reaching
out to professionals. The IISc has evolved several mechanisms to make the expertise and facilities
available to qualified technical people in industries, Universities and research establishments. The
need for forging links between academic institutions and industries and R&D organizations has
been a goal set for the IISc by its illustrious founder, J.N. Tata. CCE-PROFICIENCE was
established with the objective of providing a sustained and rigorous continuing education program
offering courses on subjects of topical interest to scientists and engineers in and around Bangalore.
This program, believed to be the first of its kind in the country, is a joint venture between IISc and
several Professional Institutions/Societies in Bangalore. The program name signifies the coming
together of Professional Institutions and the Indian Institute of Science. It was started on an
experimental basis in 1980 and has proved to be extremely popular and has attracted wide attention
in academic and professional circles. The demand for some courses, especially on computers,
microprocessors and management is so overwhelming that it has not been possible to admit all the
Eligible applicants. Every year, there has been a steady increase in the number of students as well
as the types of courses offered indicative of the growing popularity of this Program. IISc is the
custodian of the academic standards of all CCE-PROFICIENCE courses. It has the responsibility
of evolving appropriate teaching norms, providing the venue and facilities for conducting courses,
organizing the tests and examinations and issuing certificates to the successful participants. These
tasks are coordinated by the Centre for Continuing Education (CCE).
COURSES
Continuing education program organized under CCE-PROFICIENCE offers semester long courses
in areas of topical interest. The courses are organized during evening hours so that working
professionals can participate without getting their normal work affected. All courses are normally
at the postgraduate level and many of these are in fact offered to the IISc students regularly.
Participants in certain selected courses are provided practical training in computer and other
laboratories, as appropriate. The course contents are regularly upgraded on the basis of feedback
from the faculty and the participants. Courses are offered during the period AUG-DEC and JAN-
MAY and around 15-20 courses are scheduled during each semester. Each course has lectures at
the rate of two or three hours per week depending upon the number of course credits. Tests and
examinations are conducted according to the IISc norms. A series of courses leading to different
specializations are offered in a sequential manner, especially in the area of Computer Science and
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Engineering. This would enable the participants who start with the entry level courses progress
towards more advanced ones and specialize in one of the streams.
EVALUATION
The total marks for assessment will be equally distributed between the seasonal work and end
semester examination. The seasonal work consists of class tests, mid semester examination, and
homework assignments etc. as determined by the instructor. The participants who maintain a
minimum of 75% attendance both in the theory and computer/laboratory classes will be evaluated
based on the combined performance in the end semester examination and seasonal work and
assigned a letter grade.
NO RE-EXAMINATION SHALL BE CONDUCTED UNDER ANY CIR
CUMSTANCES.
The letter grades carry a 10 point grading assessment as indicated below
Grade: A+ A B+ B C D F (Fail)
Grade Points: 10 9 8 7 6 5 0
CERTIFICATES
Certificates will be issued only to those who get at least a ‘D’ grade. Attendance certificates shall
not be issued to anyone. This being a continuing education program meant especially for self-
improvement, the credits accumulated cannot be equated with the credits earned through formal
education. There shall be no claims for CCE-PROFICIENCE credits being counted towards partial
fulfillment of credit requirements towards any degree/diploma or other formal recognitions offered
by IISc.
Formal Course completion certificates will not be issued under any circumstances to any candidate.
FACULTY
The instructors for the courses are mostly Institute Faculty. However, competent professionals
from other R&D organizations and industries are also involved in teaching some of the courses.
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INSTRUCTIONS
HOW TO APPLY:
Details of the courses are available online at cce.iisc.ac.in and also directly at CCE application portal
https://iisc.online/admissions/home.html . Essential Qualification for any course is a degree in
Engineering or a postgraduate degree in Science/Humanities as applicable with pre-requisites. Each
participant will be admitted for a Maximum of Two Courses. Applying to courses is strictly through
online portal of CCE. Please read all the instructions provided at our portal before applying.
Payment of course fee is through payment gateway provided at our online portal and no other means
of payment is accepted. The course fee is Rs. 5000/- per credit and registration fee is Rs. 300/- per
course. Any other gateway charges must be borne by participant during online payment. For each
application, participants must upload (BE, B.Tech / Post Graduation) Convocation/Degree
Certificate without fail. (Class conducted: Week days 6 pm. to 8 pm) & (Saturday’s 10 am to 1 pm
& 2 pm to 4 pm)
FEES
The course fee is Rs. 5000/- per credit. Some of the courses include a limited exposure to computer
operation and programming / Lab Fee (C). The additional fees of this are Rs. 5,000/- The course fee
and laboratory fee should be paid in full at the time of joining the course.
REFUND OF COURSE FEE
Refund of course fee will not be made, unless the course is withdrawn officially, in which case, the
course fee paid will be refunded in full. Application registration fee once paid will NOT BE
REFUNDED under any circumstance. Refund of fees in case of dropped courses will take minimum
3-4 weeks.
CLASSES
Classes will be held online via Microsoft Teams.
LABORATORY CLASSES
The timings and days for laboratory classes will be fixed in the second week of the respective
months (August & January) after the complete registration is known. This will be done, keeping in
view the convenience of the faculty and all the students of the courses with laboratory component.
RESULTS
Results of the courses will be announced normally around 1st week of January for August-
December term and 1st week of May for January-May term. Certificates will be issued on or after
the date of announcement of results and against surrendering the Identity Card.
NO REQUEST FOR CHANGE OF EITHER THE STIPULATED DATES, MODE OF
PAYMENT, CHANGE OF COURSE OR SUBMISSION/VERIFICATION OF
ENCLOSURE TO APPLICATION ETC., WILL BE ENTERTAINED UNDER ANY
CIRCUMSTANCE
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Schedule of Online Courses for August – December 2021 Sl.
No. Name of the Course Credit Faculty Department
1. Innovation and Design (Mon) (6 pm to 8 pm) 2:0
Prof. P Achutha Rao
(Retd.) and Dr. J E
Diwakar (Retd.)
NID, R&D and
CPDM, IISc
2. Quantum Theory (Mon & Wed) (6 pm to 7.30
pm) 3:0 Prof. Tanmoy Das
Department of
Physics, IISc
3. Soil Dynamics (Mon & Wed) (6 pm to 7.30
pm) 3:0 Prof. Jyant Kumar
Dept. of Civil
Engg., IISc
4. Applied Linear Algebra for Engineers and
Researchers (Sun) (10 am to 12 pm) 2:0
Mr. M Krishna
Kumar (Retd.),
Dr. Arulalan
Rajan and Dr.
Ashok Rao
(Retd.)
Dept. of ESE
(CEDT), NITK
Surathkal &
DESE
5. Service Design Thinking (Tue) (6 pm – 8 pm) 2:0
Prof. P. Achutha
Rao (Retd.), Prof.
T V P Chowdry &
Dr. J E Diwakar
(Retd.)
CPDM, NID
R&D Campus &
CST
6. Vaccine and Immunity (Tue & Thu) (6 pm to
7.30 pm) 3:0
Prof. Dipshikha
Chakravortty
Dept. of MCB
IISc
7. Vibration and Noise: Theory and Practice
(Wed) (6 pm – 8 pm) 2:0 Dr. S B Kandagal AE, IISc
8. Probability Foundations for Machine Learning
(Fri) (6 pm – 8 pm) 2:0
Mr. M Krishna
Kumar, (Retd). Dr.
Ashok Rao, & Dr.
Arulalan Rajan
DESE, IISc &
NIT, Surathkal
9. Analysis and Design of Composite Structures
(Fri) (6 pm – 8 pm) 2:0 Dr. G Narayana
Naik AE, IISc
10. One and Two-Dimensional NMR Spectroscopy
for Chemists (Sat) (10 am to 1 pm) 3:0
Prof. N.
Suryaprakash
NMR Research
Centre, IISc
11. Artificial Intelligence (Sat) (10 am to 1 pm) 3:0
Prof. M Narasimha
Murthy (Honorary
Professor)
CSA, IISc
12. Data Structures and Graph Analytics (Sat) (10
am – 12 pm) 2:0 Prof. Y N Srikant CSA, IISc
13. Project Management (Sat) (10 am to 12 pm) 2:0
Prof. T V P
Chowdry and Dr.
J. E. Diwakar
CST and CPDM,
IISc
14. Introduction to Computing for AI & Machine
Learning (Sat) (10 am to 1 pm) 3:0
Prof. Sashikumaar
Ganesan
Dept. of CDS,
IISc
15. Deep Learning (Sat) (10 am to 1 pm) 3:0 Dr. V. Susheela Devi Dept of CSA,
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IISc
16. Start-up Tools (SuT) (Sat) (10 am to 1 pm) 3:0
Dr. R.N. Narahari
and Prof SA
Shivashankar
CeNSE,IISc
17. Structural Analysis and Design Optimization:
Theory and Practice (Sat) (12 pm – 2pm) 2:0 Dr. S B Kandagal AE, IISc
18. Foundations of Data Science and Machine
Learning (Sat) (1 pm to 4 pm) 3:0
Dr. Deepak N
Subramani
Dept. of CDS,
IISc
19. Business Analytics with Management Science
Models and Methods (Sun) (10 am – 1 pm) 3:0 Dr. M Mathirajan
Dept. of MS,
IISc
20. Reinforcement and Deep Reinforcement
Learning (Sat) (1 pm – 4 pm) 3:0
Prof. Shalabh
Bhatnagar CSA, IISc
21. Mathematics for Machine Learning (Sat) (2 pm
– 5 pm) 3:0
Prof. Kunal
Narayan
Chaudhury
EE, IISc
22. Introduction to AI & Machine Learning (Sat)
(1 pm – 4 pm) 3:0
Dr. Pradipta
Biswas CPDM, IISc
23. Fundamentals of Machine Learning (Sat) (10
am – 1 pm) 3:0
Dr. Gopal Krishna
Sharma, Dr.
Badarinath Ambati
& Prof. M Sekhar
Fiserv India
Ltd., Altair
Engineering, &
Civil Engg.
24. Embedded Systems and IoT Sensors
Applications (Sat) (2 pm to 4 pm) 2:0
Mr. M Krishna
Kumar (Retd.) &
Mr. S M
Narasimhan
DESE, IISc
25.
Foundations of Intellectual Property Rights
(IPRs) – Legal and policy perspective (Sat) (2
pm – 4 pm)
2:0
Prof. Anjula
Gurtoo & Dr
Akriti Jain
Dept. of MS
26. Machine Learning (Sun) (10 am to 1 pm) 3:0
Prof. M Narasimha
Murthy (Honorary
Professor)
CSA, IISc
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FEE STRUCTURE AT A GLANCE
Regular Courses
Per Credit: Rs.5, 000/-
Computer Lab Fee: Rs.5, 000/-
1. Course with 2 credits# Rs. 10,000/-
2. Course with 2+C credits # Rs. 15,000/-
3. Course with 3+0 credits # Rs. 15,000/-
4. L Stands with 2+L Credits # Rs. 15,000/-
# Credits = Lecture Hours per week
$C Stands for Computer Laboratory
$L Stands for Online Course
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01. Online Course on Innovation and Design (2:0)
Objectives:
Innovation is imperative. Innovation is a main driver of economic progress and social well-being. Innovation
typically suggests the commercialization of new ideas in ways that create social and economic values.
Innovation is also synonymous with creativity, design, artistic imagination, and cultural change. Most
management practices seek predictability, innovation is dealing with possibilities. Transforming from
'Predictability- centric' to 'POSSIBILITIES- centric' culture is a struggle. There is an urgent need to discard the
past proven methods, and to create a new culture of innovation across the organization. There can be design
without innovation, but there is no innovation without Design. To succeed, organization has to integrate
Design. This Course, through theory classes, aims to look at these issues and create an awareness of innovation
by design and various design methods in the manufacturing context.
Syllabus:
Design, Design Thinking, Innovation; Creativity Thinking, Skills and methods; New Product Development -
Quality Function Deployment, Value Engineering, Design to Cost, Design for Assembly, Design for Service,
Failure Modes and Effects Analysis; TRIZ (Systematic Innovation)- Overview; Concept Generation Methods,
Concept Selection Methods.
Target Group:
Engineers working in manufacturing industries, Practicing Engineers, Managers involved in Innovation and
New Product development, Design and Development in Industries, R & D Organizations etc., Academic
Personnel in teaching/practicing Product design, Product engineering, Design and Development and fresh
engineers interested in Design and Innovation.
Faculty:
Dr. J E Diwakar
(Retd.)
CPDM
lISc., Bengaluru.
Email: [email protected],
Faculty:
Prof. P Achutha Rao Retired from NID R & D
Campus, Bengaluru. E-Mail:
Reference Books:
1. Thomas Lockwood, Edgar Papke; Innovation by
Design; New Page Books,2017
2. Larry Kelly, Helen Walters; Ten types of Innovation,
Wiley, 2013
3. Vijay Kumar,101 Design Methods; Wiley,2012
4. Top Kelly, The Art of Innovation; Profile, 2016
Who can apply?
Graduation in Engineering, Design (B.
Des) & Post-Graduation in Management and
Design after engineering
Course Fee: Rs. 10,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Monday’s - 6.00 pm. to 8.00 pm
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02. Online Course on Quantum Theory (3:0)
Objectives:
This course is aimed at post BE and post MSc academicians as well as for those advanced students and young
faculties and R&D personals who are interested to learn, revise, and/or obtain a different perspective on
the beautiful paradigm of quantum mechanics which shook the world in the early 20th century. For the
young faculties who are in teaching profession, the course might help them getting a different and
alternative perspective to teach, think, and learn quantum theory. I will also extend the quantum theory to
current research topics of non- conserving, non-Hermitian systems
Syllabus:
Historical foundations and experimental facts. Converting Classical mechanics to Quantum mechanics:
Classical Hamiltonian becoming quantum Hamiltonian, Noether Symmetries giving uncertainly principles,
Poisson bracket becoming Commutator, Liouville's theorem becoming Schrodinger equation. Converting
Statistical Mechanics to quantum mechanics: Probabilistic description of dynamics, Building ensemble
density with wave function, Probability current. Postulates of quantum mechanics. Wave packets/Coherent
States. One-dimensional problems: step, barrier and delta-function potentials. Tunnelling, scattering and
bound states. Mathematical preliminaries: Vector space, Hilbert space, Operator, Hamiltonian, Eigenvalue.
Harmonic oscillator, operator approach. Matrix formulation of quantum mechanics. Hermitian and unitary
operators. Orthonormal basis. Non-Hermitian Hamiltonian, Exceptional points, Bi orthogonal basis
Heisenberg representation. Ehrenfest's theorem. Three-dimensional problems. Rotations, angular
momentum operators, commutation relations. Spherical harmonics. Hydrogen atom, its spectrum and wave
functions. Symmetries and degeneracies. Spin angular momentum. Spin-1/2 and two-level systems.
Addition of angular momentum. Spin-orbit and hyperfine interactions. Time independent perturbation
theory. Stark and Zeeman effects. Variational methods, ground state of helium atom.
Target Group:
Not directly relevant to Industry
Faculty:
Dr. Tanmoy Das Department of Physics
Indian Institute of Science,
Bangalore 560012
[email protected], [email protected]
Reference Books:
1. Quantum mechanics, Albert Messiah, Dover
publishers, 1999
2. Quantum mechanics, Eugen Merzbacher,
Willey,1970
3. Principles of Quantum
Mechanics, Ramamurti Shankar, Plenum Press,1980
Who can apply? BE/M.Sc
Pre- Requisites: Classical Mechanics
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Monday and Wednesday
(6.00PM to 7.30PM)
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03. Online Course on Soil Dynamics (3:0)
Objectives:
To train a Civil Engineering Professional in the field of Vibration, Wave Propagation, Determination of Dynamic
Properties of Soils, Earthquake Engineering, Machine Foundation Design, Determination of Liquefaction Resistance,
Vibration Isolation, On-destructive testing by using different Geophysical Methods. Seismic Bearing Capacity of
Foundations.
Syllabus:
Fundamental of vibrations; analysis of free and forced vibrations using spring dashpot model; equations' formulation and
solution; block vibration test for determining stiffness and damping coefficient of soil mass; formulation of the problem
for the multi-degree freedom system. Wave propagation in bar and elastic media; different types of waves; dynamic tests
for determination of elastic and shear modulus. Theories for foundations on elastic half space; effect of different pressure
distribution; comparison with spring-dashpot model. Geophysical survey using reflection, refraction, steady state
vibration, spectral analysis of surface waves (SASW) & multichannel analysis of surface (MASW) tests, and cross hole
shear tests. Liquefaction analysis; cyclic shear test; assessment of zone of liquefaction. Seismic bearing capacity of
foundations and seismic earth pressures. Vibration isolation.
Target Group:
All teachers from different engineering Colleges including NIT's and liT’s, Central Road Research Institute, New Delhi,
Central Building Research Institute, Roorkee, CSRMS, New Delhi, Civil/Geotechnical engineers from different public
undertaking and pvt.companies {BHEL, Coal Indian Limited, NBCC, NHPC, NTPC, Engineers, Engineers India Limited
, Stup Consultant, AFCON,Indian Geotechnical Society.)
Faculty:
Prof. Jyant Kumar
Dept. of Civil Engineering
Indian Institute of Science
Bengaluru (lndia)-560012
Reference Books:
• Richart, F.E., Woods, R.D.and Hall, J.R. Vibrations of
soils and foundations. Prentice-Hall,1970.
• Major, A Vibration Analysis and Design of
Foundations for Machines and Turbines.Collets,1962.
• Kramer, Steven L. Geotechnical Earthquake
Engineering. Prentice Hall,
653 pp.
Who can apply?
B.Tech-Civil Engineering
Pre- Requisites: B.Tech-Civil Engineering
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule Monday and Wednesday
(6.00PM to 7.30PM)
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04. Online Course on Applied Linear Algebra for Engineers and Researchers
(2:0)
Objectives:
This course aims at providing an in depth understanding of Linear Algebra and applications in various domains like image
processing, data fitting, data classification and machine learning. The focus of the course is primarily on geometric approach
to linear algebra and applications that involve concepts of linear algebra.
Syllabus:
System of Linear Equations, Vector spaces, Linear independence, basis, rank, linear transformations, Inner products,
Orthogonality, Orthonormal basis, Orthogonal projections and Orthogonal complements, Matrix decompositions -
eigenvalues and eigenvectors, QR, SVD, Vector calculus and gradients of matrices. Applications: Discrete Fourier
Transform, Discrete Cosine Transform and applications in image processing, least squares curve fitting, least squares
classification, Subspace’s techniques and PCA.
Target Group:
Engineers and researchers in working in the domains of Computer Vision, Machine Learning, Communication Engg,Signal
Processing, Data Science etc.
Faculty: Mr. M Krishna Kumar
(Retd.), PRS., Dept. of
ESE (CEDT), IISc,
Bengaluru
Email.
mkkumarcedt@gmai
l.com
Faculty:
Dr. Arulalan Rajan, Formerly, Assistant
Prof., Dept. of E& C
Engg., NITK.,
Surathkal
Email:
Faculty: Dr. Ashok Rao,
Formerly Head, Networking Project,
Dept. of ESE (CEDT),
IISc., Bengaluru
Email: ashokrao.mys@gmail.
com
Reference Books:
1. Gilbert Strang, Introduction to Linear Algebra,
Wellesley Cambridge Press,5th Ed., 2015.
2. Stephen Boyd, Introduction to Applied Linear
algebra, Cambridge University, 2018
3. Deise north, Mathematics for Machine
Learning, Cambridge University Press, 2020.
4. NPTEL lectures of Prof. Vittal Rao on
Advanced Matrix Theory and MIT Open
Courseware lectures of Prof. Gilbert Strang on
Linear algebra.
Who can apply?
BE/B.Tech
Pre- Requisites:
Mathematics at undergraduate level
Course Fee: Rs. 10,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Sunday’s – 10.00am to 12.00pm
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05. Online Course on Service Design Thinking (2:0)
Objective
The globalization and digital connectivity have forced many organizations to look at the way new products/services are to
be developed for customer acceptance in the changed competitive “global digital world’. The economy is shifting from
manufacturing economy to service and knowledge economy. Service economy is an economy based on providing services
rather than manufacturing or producing goods. There is increased importance of the service sector in industrialized
economies. The current list of fortune 500 companies contains more service companies and fewer manufactures than in
previous decades. Many products are being transformed into services. Design of services is gaining more prominence and
becoming a specialized field of expertise. This course will look at various aspects of service design thinking. The participants
will become aware of all aspects of service design.
Syllabus:
• Innovation, Creativity, Embedded Mindset to Creative Thinking, Barriers to Creativity.
• Product Economy to Service Economy
• Service Design Thinking
• Fields of Service Design
• Principles of Service Design
• Marketing Connecting with people, Creating Customer Value
• Product Design: Developing Products with Service Applications
• Social Design: Delivering Positive Social Impact
• Strategic Management
• Operations Management
• Tools of Service Design Thinking
Target Group:
Practicing Engineers, Managers Responsible for developing engineering services, Professional in Design and Development
in Industries, R & D Organizations etc., Academic Personal in teaching/practicing Product design/Service design, Product
engineering, Design and Development and fresh engineers interested in Design and Innovation; Start up entrepreneurs.
Faculty:
Dr. J. E. Diwakar (Retd.)
Dept. of CPDM.,
IISc., Bengaluru.
Email: [email protected]
Faculty:
Prof. P. Achutha
Rao (Retd.) NID R & D Campus.
E Mail: [email protected]
Faculty:
Prof. TVP
Chowdry Project Scientist CST
E mail: [email protected]
1.
Reference Books:
1. Marc Stickdorn,
This is Service Design Thinking: Basics-Tools-Cases.
Wiley.
2. Andy Polaine, Rosenfeld Media,
Service Design from Insight to Implementation.
3. Tim Brown,
Change by Design: How Design Transforms Organizations
and Inspires Innovation.
4. Satu Miettinen & Mikko Koivisto,Designing Services
with Innovative Methods: University of Art and Design
Helsinki
Who Can apply?
Graduation in Engineering, Any Master’s
Degree after engineering.
Course Fee: Rs. 10,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Tuesday’s: 6.00 pm. to 8.00 pm
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06. Online Course on Vaccine and Immunity (3:0)
Objectives:
This course will deal with in depth understanding of vaccine, vaccine development, vaccine
engineering and finally about the immunity by vaccines
Syllabus:
History of Vaccine development, Types of vaccines, bacterial and viral vaccine, Genetic engineering techniques to develop vaccine, how vaccine works.
Target Group:
Everybody in Biotech industry with vaccine manufacturing, will benefit,
Faculty:
Prof. Dipshikha Chakravortty
Professor
Department of Microbiology and Cell Biology
Indian Institute of Science
Bangalore 560 012 [email protected]
Reference Books:
1. Kuby's Immunology, Janis Kuby, New York WH
Freeman publications Principles of Vaccination,
CDC, Updated on 2020
Who can apply?
M.Sc in any branch of Science
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule Tuesday and Thursday (6.00PM to
7.30PM)
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07. Online Course on Vibration and Noise Control: Theory & Practice (2:0)
Objectives: Growing awareness of vibration, noise and harshness feeling has necessitated the valid design criterion in the design of
machines, automobiles, buildings, industrial facilities, etc., and the increasing number of standard regulations and human
comfort associated with noise, harshness and vibration makes it mandatory to control vibration and noise leading to quieter
technology in pumps, engines, compressors, chillers and other consumer products. There is a great demand to enhance ride
comfort of bikes, cars, aircrafts and other automobiles. Vehicle Dynamics basics and rowing awareness about noise pollution
among the consumer necessitates the OEM companies to stress upon the products without NVH problems. Analytical,
MATLAB and FEM based tools such as ANSYS, NASTRON, ABACUS and SYSNOISE helps to achieve the goals of NVH
study. This course is for engineers/scientists/entrepreneurs/instructors in the industries/institutes to learn the analytical and
experimental skills to tackle the problems related noise, vibration and harshness (NVH) during design and manufacturing
stage for technically superior and commercially viable product to achieve “EMPOWER INDIA WITH SKILL AND
Knowledge”
Syllabus:
Vibration of structural systems. SDOF, 2-DOF, MDOF and continuous systems. Eigen values and vector estimation methods. Free and
Forced vibration analysis. Torsional vibration and applications. Damping estimation methods
Structural Vibration control elements: isolation, damping, balancing, resonators, absorption, barriers and enclosures. Vibration and
noise standards. NVH measurement tools and techniques. Modal parameter (natural frequency, mode shape and damping) estimation
techniques. Signal and system analysis.
Demonstration of vibration and noise experiments – beam, plates, impulse excitation, electrodynamic shaker excitation, FFT analyzer,
stroboscope and mode shape animation, sound level meter, microphones. Vibration transfer function (VTF) and noise transfer function
(NTF)Noise and its effects on man. Acoustic and sound field. Enclosures, shields and barriers-design. Silencer and suppression systems.
Noise level interpolation and mapping. Harshness effects and measurements and solutions. NVH Parameters related to vehicle dynamics
Case studies discussion (vibration reduction in passenger car, tiller, tractors, steering column/wheel vibration diagnosis, Modal analysis
of Helicopter, Vibration diagnosis in diesel engine power plant, rotodynamic analysis of DWR and tracking antenna and engine and
compressor noise attenuation and vibration isolation, engine-compressor mount design, vibration diagnosis in power plants, gear shift
harshness, newspaper printing cylinder vibration diagnosis, engine filter bracket dynamic analysis, noise reduction for mixer grinders, field
audit of industrial chimney for wind induced vibration, stability studies of sports bike, aerodynamic stability derivatives of scaled model
of aerospace vehicles)
Target Group:
Mechanical, Civil, Aerospace, Automotive, Industrial Engineers, Construction Technologists, R & D Labs, New product Design and
Development Groups, Entrepreneurs and Engineering College Instructors. Professionals to pursue Postgraduate and Higher Studies
Faculty:
Dr. S B Kandagal Principal Research Scientist,
Dept. of AE.,
IISc., Bengaluru.
Email: [email protected]
Reference Books:
1. Harris, C.W”,
Shock and Vibration Handbook”
McGraw Hill, New York, 2012.
2. Ewins, D.J.
” Modal analysis: Theory and Practice”,
Research Studies Press Ltd, England, 2014
3. Gillespie, T.D.,
“Fundamentals of Vehicle Dynamics”,
Society of Automotive Engineers. Inc, 2010.
4. Beranek, L.L,
” Noise and Vibration Control”, Wiley, 2008
Who Can apply?
B.E / ME / MSc / AMIE OR equivalent
Course Fee: Rs. 10,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Wednesday’s - 6.00 pm. to 8.00 pm.
Page 16 of 36
08. Online Course on Probability Foundations for Machine Learning (2:0)
Objectives:
This course aims to provide an in Depth Understanding of Probability Concepts for Engineers and Professionals
Working in Various Domains Including Machines Learning, Data Science.
Syllabus:
Probability and Counting Conditional Probability Independence of events, Discrete and Continuous Random
Variables and their Distributions, Independence of Random Variables, Joint Distributions Joint Expectations
Covariance Moments, Conditional Expectations, Limit Theorems Random Vectors, Covariance Matrix, Sample
Geometry and Random Sampling, Multivariate Normal Distribution.Parameter Estimation Regression Models.
Random Processes, Stationarity, Markov Chains, Bernoulli and Poisson Processes, Some Applications.
Target Group:
Engineers and Researchers.
Faculty: Mr. M Krishna Kumar
(Retd.),
PRS., Dept. of ESE (CEDT),
IISc., Bengaluru
Email.
m
Faculty: Dr. Arulalan Rajan,
Formerly, Assistant
Prof., Dept. of E& C Engg.,
NITK.., Surathkal..
Email: [email protected]
Faculty: Dr. Ashok Rao,
Formerly Head, Networking Project,
Dept. of ESE (CEDT),
IISc., Bengaluru
Email: ashokrao.mys@gmail.
com
Reference Books:
1. Bertsekas, Tsitsiklis
Introduction to Probability, 2nd Ed. Athena Scientific.
2. Roy D Yates, D J Goodman
Probability and Stochastic Processes, John
Wiley. 2014.
3. Steven M Kay
Intuitive Provability and Random Processes, Springer.
4. R. Johnson, D.A Wichern,
“Applied Multivariate Statistical Analysis”, Pearson,
2015.
Who can apply?
BE/ MSc.
Pre-requisites: Mathematics at Graduate Level
Course Fee: Rs. 10,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Friday’s -6.00 pm - 8.00 pm.
Page 17 of 36
09. Online Course on Analysis and Design of Composite Structures (2:0)
Objectives:
Composites are future materials and have been finding applications in all fields of Engineering (Aero, Civil, Mechanical,
Automobile, Marine, Chemical, Electrical, Electronics etc). Many FEM software packages like ANSYS, MSC-NASTRON,
PATRAN, ABACUS, LS-DYNA, etc are available for Analysis & Design Optimization. One should first understand the
Mechanical behavior of the Composite Structures before using FEM packages. After the completion of this course one can
use the FEM software packages for better quality of professional work and optimum usage of time, computing and human
resources. Syllabus:
Introduction: Basic Concepts and Terminology, different types of fibers and matrices, their properties and applications.
Micromechanics of Composites: Prediction of Elastic and strength properties, Micromechanical failure etc.
Macromechanics of Lamina: The theory of elasticity, Constitutive equations of a lamina, transformations, numerical
examples.
Failure theories for composite lamina, numerical examples.
Mechanics of Laminated Composites: ABD matrices, etc. Hygrothermal Analysis
Analysis of Composite Beams: theory and numerical examples.
Bending Analysis of Beams,
Analysis of Laminated composite plates: Classical and first order theories, Energy Method, numerical examples.
Buckling analysis of plates: theory and numerical examples.
Design of laminates using Carpet plots, AML plots, laminate design using numerical examples.
Target Group:
1. Technologists/ Engineers/ Scientists/ Trainees/ Project Staff/ etc. from Industries, R & D Organizations, Institutions,
Colleges etc.
2. Faculty of Engineering// Diploma Institutions etc.
3. Fresh Graduates, Post Graduates, Ph.D. Students, Research Fellows, SRFs, JRFs, etc.
Faculty:
Dr. G. Narayana Naik Principal Research Scientist,
Dept. of AE.,
IISc., Bengaluru.
Email: [email protected]
Reference Books:
1. Madhujit Mukhopadhyay, Mechanics of Composite
Materials and Structures- Universities Press- Engg.
2004.
2. Zafer Gurdal, Raphael T Haftka, Design and
Optimization of Laminated Composite Materials,
John Wiley & Sons, INC – 1999.
3. J.N.Reddy, Mechanics of Laminated Composite
Plates and Shells Theory and Analysis – CRC Press
– 2004.
Who Can apply?
B.E / B.Tech. / AMIE / M.Sc.(Engg.)/ AMAeSI
(Engg.) (Mechanical, Aero, Civil, Automobile,
Marine, Ocean) OR equivalent.
Course Fee: Rs. 10,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Friday’s - 6.00 pm. to 8.00 pm.
Page 18 of 36
10. Online Course on One- and Two-Dimensional NMR Spectroscopy for
Chemists (3:0)
Objectives:
NMR spectroscopy is a ubiquitous technique employed in various branches of science and engineering. Today
it is hard to do experimental chemistry and biology without the utility of NMR spectroscopy. The real strength
of NMR lies in probing the very weak interactions surrounding the resonating spins. It provides information on
the molecular structure, conformation and dynamics. It is essential for all the practicing chemists and research
scholars to clearly understand the concepts and interpretation of NMR spectra. At present the deeper
understanding of NMR spectroscopy is residing with only few active NMR groups of the country. This course
will provide exhaustive knowledge to the participants about the latest state of art of the NMR spectroscopy.
Syllabus:
In this course, I will discuss fundamental concepts of NMR spectroscopy, experimental determination of NMR
spectral parameters, their interpretation, selective and broadband homo and Heteronuclear spin decoupling,
numerous examples of the analysis of NMR spectra of 1H, 13C and other heteronuclear will be given. The
relaxation processes, their measurement and utility in understanding molecular dynamics, the polarization
transfer mechanism, the spectral editing techniques, such as, APT, DEPT, INEPT will also be discussed. Two-
dimensional NMR and the commonly employed experiments, viz., COSY, TOCSY, HSQC, HMQC, HMBC,
NOESY, etc will be discussed with number of examples. The practical aspects of one- and two-dimensional
NMR data acquisition and processing will also be highlighted. The solid-state NMR, magic angle spinning and
cross polarization will also be discussed.
Target Group:
All R and D scientists of Pharma industries, MSc students, PhD students and also practising young researchers
Faculty:
Prof. N. Suryaprakash
NMR Research Centre,
Indian Institute of Science,
Bangalore 560012
Reference Books:
1. A Complete Introduction to Modern NMR
Spectroscopy: RobertS Macomber
2. Carbon-13 NMR Spectroscopy, Eberhard Breitmeier
and Wolfgang Voelter
3. High Resolution NMR, Theory and Chemical
Applications, Edwin D. Becker
4. High Resolution NMR Techniques in Organic
Chemistry, Timothy D Claridge
5. NMR Spectroscopy Explained, Neil E Jacobsen
Who can apply?
MSc in chemistry, M Pharm, MSc in
Physics
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s - 10.00 am. to 1.00 pm
Page 19 of 36
Objectives:
To introduce both the symbolic and connectionist paradigms of Artificial Intelligence. The course will
be self-contained. The aim is to cater to the needs of both researchers and developers.
To introduce both the symbolic and connectionist paradigms of Artificial Intelligence. The course will
be self-contained. The idea is cater to the needs of both researchers and developers.
Syllabus:
Search for problem solving. Inference based on Logic and Probability; Theorem Proving and Prolog.
Data Structures and LISP. Machine Learning: Neighborhood based, and Linear Discriminants. Neural
Networks and Back Propagation. Deep Learning
Target Group:
Any academic or R&D organisation with potential interest in AI.
.
11. Online Course on Artificial Intelligence (3+0)
Faculty:
Prof. M Narasimha Murty
Honorary Professor,
Dept. of CSA.,
IISc, Bengaluru.
Email: [email protected]
Reference Books:
1. Russell and Norvig, Artificial Intelligence: A
Modern Approach, Pearson,2015.
2. M N Murty and VS Devi, Introduction to
Pattern Recognition and Machine Learning,
lISe Press, World-Scientific,2015.
3. Mitesh Khapra,Deep Learning, NPTEL, lIT
Madras, 2018.
Who can apply?
BE/B.Tech or its equivalent in any branch;
Master of Science or equivalent in Maths, Physics,
Computer Science, Statistics; or MCA.;
Pre-requisites:
A good background of college-level mathematics and
programming.
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s – 10.00 am. to 1.00 pm
Page 20 of 36
12. Online Course on Data Structures and Graph Analytics (2:0)
Objectives:
Graph Analytics is important in different domains: Social Networks, Computer Networks, and Computational
Biology to name a few. This course will deal with the data structures and algorithms underlying Graph Analytics.
Important applications will also be considered.
Syllabus:
Overview of data structures such as arrays, linked lists, and trees. Important data structures such as binary search
trees, balanced trees, MFSET, heap, hash tables, game trees, etc. Algorithm design paradigms: back-tracking, divide
and conquer, dynamic programming, and greedy. Graph analytics: shortest path computation, minimum spanning
trees, graph matching, network flows, centrality computations, community detection, connection analysis, etc.
Target Group:
Persons in R&D and final year M.Tech. Students.
Faculty:
Prof. Y.N. Srikant,
Dept. of CSA
IISc., Bengaluru.
Email: [email protected], [email protected]
Reference Books:
1. T H Cormen, C E Leiserson, and R L Rivest,
Introduction to Algorithms, The MIT Press,
Cambridge, Massachusetts, USA, 1990.
2. Unnikrishnan, Cheramangalath, Rupesh Nasre, and Y
N Srikant, Distributed Graph Analytics:
Programming, Languages, and their Compilation,
Springer, 2020.
3. Amy E, Hodler, and Mark Needham, Graph
Algorithms: Practical Examples in Apache Spark and
Neo4j. O’Reilly, 2019 (free book, downloadable).
Who Can apply?
MCA/MSc/BTech/MTech (any discipline) with
good knowledge of programming in
C/C++/Java/Python (any one)
Pre-requisites:
Good knowledge of programming in
C/C++/Java/Python (any one)
Course Fee: Rs. 10,000/- + 18% GST
Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s - 10.00 am. to 12.00 pm
Page 21 of 36
13. Online Course on Project Management (2:0)
Objectives:
This course is envisaged to develop the understanding and skills for planning, scheduling and controlling
projects. This course provides a systematic and thorough introduction to all aspects of project management.
Students explore project management with a practical, hands-on approach through case studies and class
exercises. The understanding and skills gained in this course will help in Project Management Institute’s (PMI)
Project Management Professional (PMP) Program.
Syllabus:
• What is Project Management?
• Organizing Project Management Office & Team
• Project Planning, Work Break Down Structure
• Project Budgeting- Cost Estimation
• Project Scheduling- Gantt Chart PERT/CPM
• Project Resource Allocation- Fast Tracking- Crashing o Resource Loading & Levelling
• Project Management & Controlling & Earned Value Scope Creep and Change Control
• Project Evaluation & Termination
Target Group:
Practicing Engineers, Scientists, R&D Managers, Construction Managers, Architects, Designers, Professionals
from Knowledge & IT Industries, Entrepreneurs
Faculty:
Dr.J. E. Diwakar Dept. of CPDM., IISc.,
Bengaluru
Email: [email protected]
Faculty: Prof TVP Chowdry. Project Scientist
CST, IISc
Email:
Reference Books:
1. Meredith R Jack, & Mantel J Samuel, Project
Management: International Student Version, Eighth
Edition, Wiley,2006, ISBN 978-81-265-3708-2
2. Project Management Institute, A Guide to the project
Management Body of Knowledge: {PMBOK® Guide)
Sixth edition, PMI,2018, ISBN 9781628253825
3. Charted Management Institute, Successful Project
Management, 2nd edition Elsevier, 2004, ISBN 0-7506-
64197
Who can apply?
Graduation in
Engineering/Architecture/Design or Post
Graduation in Science/Management
Course Fee: Rs. 10,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s 10.00 pm. to 12.00pm
Page 22 of 36
14. Online Introduction to Computing for AI & Machine Learning (3:0)
Objectives:
This course is aimed at building the foundation of computational thinking with applications to Artificial
Intelligence and Machine learning (AI & ML). Besides, how to build a neural network and how to train, evaluate
and optimize it with TensorFlow will also be covered in this course.
Syllabus:
Programming Foundation: Digital storage of data in computers, memory and data representation, Overflow and
Underflow, Round-off errors, the performance of a computer, Caches, Debugging and Profiling, Basic
optimization techniques for serial code.
Introduction to Python: Object and Data Structure Basics, Python Statements, Methods and Functions, Object-
oriented programming (OOP): Inheritance, Encapsulation, Abstraction, Polymorphism. OOP concepts in Python.
Python tools for Data Science: Pandas, NumPy, Matplotlib, Scikit-Learn, Just-in-Time (JIT) compilers, Numba
Computational Thinking: Arrays, Matrix-Vector, Matrix multiplication, Solving dense and sparse systems.
Deep Learning with TensorFlow: Tensors, Install TensorFlow, TensorFlow basics, Simple statistics and
plotting, Loading and exploring data, Learning with TensorFlow and Keras,Mini-project.
Target Group:
Aspiring ML/AI and Data scientists
Faculty:
Prof. Sashikumar Ganesan
Associate Professor & Chairman,
Dept. of Computational and Data Sciences,
IISc., Bengaluru.
E-mail: [email protected]
Reference Books:
1. John Hennessy David P Patterson.Computer
Architecture.AQuantitative Approach.6th edition,
Morgan Kauffman, 2017.
https://www.elsevier.com/books/computer-
architecture/hennessyI 978-0-12-811905-1
2. Shaw, Zed A. Learn python 3 the hard way. A very
simple introduction to the terrifyingly beautiful
world of computers and code. Addison-Wesley
Professional, 2017.
3. Aurelien Geron, Hands-On Machine Learning with
Scikit-Learn, Keras,and TensorFiow,2nd Edition,
O'Reilly Media, Inc.2019
Who can apply?
Any Engineering or Masters graduates
Pre-requisites:
Basic knowledge of programming
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s (10.00 AM to 1.00
PM)
Page 23 of 36
15. Online Course Deep Learning (3:0)
Objectives:
To expose students to the concepts of deep learning. To educate them about the architectures used in
deep learning and their implementations. They will learn about architectures such as convolution
networks, recurrent neural networks, and generative models. Implementation for different architectures
and applications will be an important part of the course.
Syllabus:
Feedforward networks, convolution networks, recurrent neural networks, generative models, variational
and adversarial networks; practical issues in implementation for different applications such as text,
speech and images.
Target Group:
Industry, R&D organization, academic institutions
Faculty:
V. Susheela Devi Department of Computer Science and Automation,
lISe
Mob:9480436301
email: [email protected]
Reference Books:
• Goodfellow, Bengio and Courville, Deep Learning,
MIT Press, 2016
Who can apply?
B.Tech, M.Sc.(Computers), MCA
Pre-requisites:
Linear Algebra, Probability
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s (10.00 am to 1.00
pm)
Page 24 of 36
16. Online Course on Start-up Tools (SuT) (3:0)
Objectives:
The aim of this Course is to introduce aspirants to the broad subject of Start-up initiatives. ‘Start-up’ is the
buzz word being sprouting now and has a great potential to grow into a big wood in near future. The world at
large appears to be tilted towards micro adventures called “Start-ups”. The course envisages imparting various
skill-sets – English communication, breading IP culture, creation of IP and related aspects, seeking funds,
management skills to handle stress to sustain business, sharing live experience from founders of start-ups.
Syllabus:
The Course aims to expose the students to the basic principles and practices in the broad field of “Start-up
ventures”: Communication in general and technical writing in particular; IP and IP protection; formalities of
registration, compliances, seeking funding and so on. It also aims to equip them with the knowledge; skills and
attitude they need to overcome problems in the creation & sustaining of Start-up. It is planned to invite founders
of Start-ups to relate their own experiences.
Target Group:
All entrepreneurs, students, engineers and scientists who aspires to kindle entrepreneur in her/him.
Faculty:
Dr. R.N. Narahari Centre for Nano Science
Engineering,
Indian Institute of Science
Email:, [email protected],
Faculty:
Prof SA
Shivashankar Centre for Nano Science
Engineering,
Indian Institute of Science
Reference Books:
1. The New Business Road Test: What Entrepreneurs
and Investors should do before Launching a Lean
Start-up; John Mullins, Pearson India Education
Services PL, Noida, 2019.
2. Entrepedia: A step-by-step guide to becoming an
entrepreneur in India by Prof Nandini Vaidyanatha,
Embassy Books.
3. Taraporevala, V J “Law of Intellectual Property”
Published by V J Taraporevala, Mumbai, 2005
4. The Elements of Style by William Strunk Jr & EB
White, Longman Publishers. Ma, USA 02494
Who can apply?
MSc in chemistry, M Pharm, MSc in
Physics
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s 10.00 pm. to 1.00 pm
Page 25 of 36
17. Online Course on Structural Analysis and Design Optimization:
Theory and Practice (2:0)
Objectives:
Advanced research in material science to enhance the life with reduced cost resulted in metal alloys, plastics, composites and
nano materials. Structural design and optimization of components with unusual shapes became possible with current available
finite element software tools such as ANSYS, NISA, NASTRON, ABACUS, SYSNOISE, LSDYNA and MATLAB etc.
The fundamental knowledge of stress, strain, shear, torsion in relation to the structures and S-N curves in relation to the
material fatigue life becomes important. The interpretation of the FEM software output calls for the knowledge of analysis
and design optimization of mechanical systems. This course essentially trains engineers/scientists/entrepreneurs/instructors
in the industries/institutes to optimally design various mechanical systems and sub-systems for technically superior and
commercially viable value-added product and achieve “EMPOWER INDIA WITH SKILL AND Knowledge”
Syllabus:
Applied mechanics, Strength of materials, SFD, BMD, AFD, solid mechanics, concept of stress, strain and fatigue.
Constitutive laws. Mohr’s Circle, Engineering materials and their properties. Structural analysis concepts, tension,
compression, shear, torsion, coupled system, and S-N curves. Design of beams, torsion, compression members and fasteners.
Stability of structures. Composite materials and their importance in structural analysis design optimization. Principles of
optimization, formulation of objective function and design constraints, classification of optimization problem. Single and
multivariable optimization. Optimization with equality and inequality constraints. Optimal design of mechanical elements –
fasteners, springs, gears, bearings, belts, clutches, brakes, shafts and axles. Procedures for product design, development and
testing. Vibration of structures
Practical problem discussion with industrial products (optimization of passenger car sub systems for vibration and noise
reduction, Rail-coach-CBC couplers, Car door window regulator, satellite tracking antenna and DWR antenna design, Tractor
canopy, hydraulic crawler driller (drilling machine), Bike brake system, sluice valve design, failure analysis if piston drill
bit, thermally insulated box, IP turbine blade failure analysis, design analysis of super pump impeller, Structural design
aspects in power plants. Hydraulic jacks/Feed cylinder with intermediate supports, Industrial chimney design, optimization
of box culverts, metal-composite sprocket for bikes, design criteria for Van pump, Thermal analysis of heat exchangers, 6-
DOF force balance, pitch flexure, roll flexure design for wind tunnel model studies for aerodynamic derivatives of aerospace
vehicle and automobiles).
Target Group:
Mechanical, Civil, Aerospace, Automotive, Industrial Engineers, R & D Labs, Construction Technologists, New product
Design and Development Groups, Entrepreneurs and Engineering College Instructors. Professionals to pursue Postgraduate
and Higher Studies
Faculty:
Dr. S B Kandagal Principal Research Scientist,
Dept of AE,
IISc., Bengaluru.
Email: [email protected]
Reference Books
1. Beer F P and Johnson, E.R,
“Vector Mechanics for Engineers- Statics and
Dynamics”, Tata-Mac Graw Hill, Sixth Edison, 2012.
2. Shigley, J.E and Mischke, C.R.,
“Mechanical Engineering Design”
Tata-Mac Graw Hill, sixth Edison, 2010.
3. Johnson Ray, C. ” Optimum Design of Mechanical Elements”,
Wiley, John & Sons, 2014.
Who Can apply?
BE, ME, MSc, AMIE, or equivalent
Course Fee: Rs. 10,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams/Google Meet
Schedule: Saturday’s 12.00 pm. to 2.00 pm.
Page 26 of 36
18. Online Course on Foundations of Data Science and Machine Learning
(3:0)
Objectives:
The course provides an accessible, yet rigorous mathematical foundation in probability, statistics, linear algebra,
and calculus required to understand data science including analytics, machine learning and deep learning. Classes
involve hands-on problem solving along with concepts being introduced through examples from the viewpoint of
data science. At the end of the course, students would have gained enough knowledge to solve problems in
foundational mathematics topics and undertake further advanced courses in Data Science, Analytics and Machine
Learning with confidence.
Syllabus:
Probability and Statistics Module: Probability axioms; Conditional Probability; Bayes' Theorem; Independence;
Counting Problems; Discrete and Continuous Random Variables; Expectation; Iterated Expectation; Total Law
of Probability; Covariance; Correlation; Entropy; Mutual Information; Frequentist Inference; Bayesian Inference.
Calculus Module: Functions; Derivatives; Multivariate Calculus; Jacobian; Hessian.
Linear Algebra Module: Vectors; Matrices; Basis; Norms; Orthonormality; Linear System, Rank, and Solution;
Linear Transformation; Matrix Multiplication; Matrix Decomposition Factorization; Cholesky Decomposition;
Singular Value Decomposition; Eigen Decomposition.
For more details check: http://cds.iisc.ac.in/faculty/deepakns/
Target Group:
All data science aspirants from industry (e.g., IT, Internet Companies, Finance, Engineering etc), R&D institutions (e.g.,
ISRO, NAL, CPRI etc)
Faculty:
Dr. Deepak N Subramani Assistant Professor,
Dept. of Computational and Data Sciences,
IISc, Bengaluru.
E-mail: [email protected]
Reference Books:
• Bertsekas, Dimitri P., and John N. Tsitsiklis.
Introduction to Probability. Vol. 1. Belmont, MA
Athena Scientific, 2002.
• Gibert Strang. Linear Algebra for
Everyone,Wellesley-Cambridge Press, 2020
• Notes and problem sets from the instructor
Who can apply?
B.Tech./M.Sc. or Equivalent
Pre-requisites:
High-School Mathematics
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s (1.00 pm to 4.00 pm)
Page 27 of 36
19. Business Analytics with Management Science Models and Methods
(3:0)
Objectives:
To provide business practitioners and those who are interested in Business Analytics a selected set of Management Science
and Optimization Techniques along with the fundamental concepts, methods, and models for understanding prescriptive-
analytics and implementation these techniques in the era of Big Data.
Syllabus:
Introduction to Business Analytics, Linear/Integer/Non-Linear Optimization, Optimization of Network Models, Dynamic
Programming, Heuristic Programming, Goal Programming, Multi-Attribute Decision Making Methods, and Monte Carlo
Simulation. These are believed to be among the most popular Prescriptive Analytics tools to solve a majority of business
optimization problems, with case studies from Business, Industry, and Government (BIG) applications using
LINDO/LINGO/CPLEX optimization package.
Target Group:
Every Business, Industry and Government (BIG) organizations which has “Business Analytics’ group to address various
problems associated with Prescriptive Analytics, In addition, all Faculty and interested UG and PG Graduates in Engineering
and Post Graduate in Business Administration/Management, Operations Research, Computer Science, Computer
Applications, Mathematics, Statistics, Economics.
Faculty:
Dr. M Mathirajan
Chief Research Scientist,
Dept. of M S.,
Faculty of Engineering,
IISc, Bengaluru.
Email: [email protected]; [email protected]
Reference Books:
1. Wayne LWinston.Operations Research:
Applications and Algorithms (Latest Edition).
Duxbury Press. An Imprint of Wadsworth
Publishing Company, Belmont, California, USA.
2. Anderson, Sweeney and Williams.
An Introduction to Management Science:
Quantitative Approaches to Decision Making
(Latest Edition). South-Western College
Publishing
3. U Dinesh Kumar,
Business Analytics:The Science of Data-Driven
Decision Making Wiley India, 2017.
Who Can apply?
BE/B.Tech., ME/M.Tech. MBA, and Master in
Operations Research, Computer Science,
Computer Applications, Mathematics, Statistics,
and Economics.
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Sunday’s – (10.00 am. to 1.00 pm.)
Page 28 of 36
20. Online Course on Reinforcement and Deep Reinforcement
Learning (3+0)
Objectives:
To provide both a rigorous foundation in Reinforcement Learning through the various tools, techniques
and algorithms used as well as cover the state-of-the-art algorithms in Deep Reinforcement Learning.
Syllabus:
Introduction to Reinforcement Learning, Multi-armed bandits, Markov decision processes and
Dynamic Programming, Model-Free Methods: Monte-Carlo and Temporal Difference approaches, Q-
learning, SARSA, Double Q-learning, Function approximation methods: TD Learning, Deep Q-
Network, Policy Gradient Approaches, TRPO, PPO, Deep Deterministic Policy Gradients,
Asynchronous Actor-Critic Algorithms.
Target Group:
People from Industry, College Teachers, Research and Project Staff will benefit.
Faculty:
Prof. Shalabh Bhatnagar
Dept. of CSA, IISc.
E-mail: [email protected]
Reference Books
1. R.Sutton and A.Barto,
Reinforcement Learning, MIT Press, 2'nd
Ed., 2018
2. D.Bertsekas,
Reinforcement Learning and Optimal
Control, Athena Scientific, 2019
3. Recent Research Papers
Who can apply?
B.Tech/MCA/M.Sc
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams/
Schedule: Saturday’s 1.00 pm to 4.00 pm
Page 29 of 36
21.Online Course on Mathematics for Machine Learning (3:0)
Objectives:
The goal is to provide a self-contained introduction to some of the core mathematics that comes up in machine
learning, namely, calculus, linear algebra, probability and optimization. The course is particularly targeted at
professionals who wish to hone their math skills and understanding of the basic concepts. We will start with
foundational topics and gradually build up to various models and algorithms used in machine learning. Problem
solving in the form of exercises and assignments will be involved; solutions will be provided.
Syllabus:
Fundamentals of multivariate calculus, linear algebra, probability and optimization; applications in machine
learning: empirical risk minimization and PAC learning, linear and nonlinear regression, clustering,
dimensionality reduction, classification, linear predictors, kernel methods, neural networks and deep learning.
Target Group:
All students, engineers, and scientists, who aspire to kindle the “entrepreneur in her/him.
Faculty:
Prof. Kunal Narayan Chaudhury
Associate Professor,
Dept. of Electrical Engineering
IISc., Bengaluru.
Email: [email protected]
Reference Books
1. Mathematics for Machine Learning by M.P.
Deisenroth, A.A. Faisal and C.S. Ong, Cambridge
University Press, 2020.
2. Understanding Machine Learning by S. Shalev-
Shwartz and S. Ben-David,
Cambridge University Press, 2014.
3. Notes provided by the instructor.
Who Can apply?
B.E./B.Tech. /M.Sc. (Math/Physics)
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s – 2.00 pm. to 5.00
pm.
Page 30 of 36
22. Online Course on Introduction to AI & Machine Learning (3:0)
Objectives:
• Introducing classical AI
• State Space Search
• Bayesian Inference
• Statistical Machine Learning
• Artificial Neural Network
• Deep Neural Network
• Image processing with DNN
• Case Studies from Autonomous Vehicle and Industry 4.0
Syllabus:
State Space Search Uncertainty Modelling Bayesian Inferencing Supervised Machine Learning - Neural
Network Clustering Cross Validation & Cluster Validation Markov Decision Process Deep Learning Basics of
Image Processing with DNN Variable Autoencoder, Generative Adversarial Network Case Studies
Target Group:
Industry practitioners, Lecturers, Early-stage researchers, scientists
Faculty:
Dr. Pradipta Biswas
Assistant Professor,
Dept. of CPDM, IISc.
E-mail: [email protected]
Reference Books:
1. Russell S and Norvig P., A Modern Approach to
Artificial Intelligence Aaron Courville 2. Ian Goodfellow, and Yoshua Bengio, Deep Learning
Who can apply?
BE, B.Tech., MSc
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s 1.00 pm to 4.00 pm
Page 31 of 36
23. Online Course on Fundamentals of Machine Learning (3:0)
Objective
To teach the basics of Machine learning, which is widely applied in almost all fields in practice.
Syllabus:
Introduction to Data Science; Review of Probability theory and Linear Algebra Classification and Regression,
Covariance and Correlation coefficient, Simple regression, Least Squares and Maximum Likelihood estimation
of parameters, residues, Hypothesis testing, ANOVA Multiple regression, Partial correlation coefficient,
Estimation of parameters, Gradient Descent and Stochastic Gradient Descent methods Classification, Linear
and nonlinear classifiers, multiple classes, Dimensionality reduction - Principal component analysis, Linear
Discriminant Analysis; Nearest Neighbour Classifiers- k Nearest Neighbor (kNN) rule, Irrelevant attributes and
scaling problems; Fundamentals of Artificial Neural Networks, Structure of an artificial neural network,
Weights and Biases, Activation function, Training a neural network -error and back propagation of error,
stochastic gradient descent for optimizing weights; Decision Trees- Concept of a decision tree as a classifier,
Entropy and Information Gain, Pruning; Naïve Bayes' classifier review of Baye's theorem, Independence
assumption and Naïve Bayes Classification; Hidden Markov models- Introduction to Markov process, Discrete
state Markov process, state transition and transition probability, Three problems of HMM; Clustering- k Mean
Clustering; Software development project using Python consisting of all the topics learnt.
Target Group:
Any Industries that deal with data analysis and management and academics interested in data science.
Faculty
Dr. Gopal Krishna
Sharma Fiserv India Pvt. Ltd.,
Bengaluru.
Email: [email protected]
Faculty
Dr. Badarinath Ambati
Altair Engineering,
Bengaluru.
Email: [email protected]
Faculty
Prof. Muddu
Sekhar Dept. of Civil
Engineering,
IISc., Bengaluru.
Email: [email protected]
Reference Books
1. Introduction to Machine Learning - Ethem
Alpaydin,2ed, MIT Press,2010 (or later)
2. Probability & Statistics with Reliability
Queuing and Computer Science Applications -
Kishore S Trivedi, 2 ed, Wiley Student edition
3. Forecasting Methods and Applications -
Spyros Makridakis et el,3ed, 2005 (or later)
Who Can apply?
Any Engineering Degree with Mathematics
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s (10.00 am to 1.00 pm)
Page 32 of 36
24. Online Course on Embedded Systems and IoT Sensors Applications
(2:0)
Objectives:
This course an Advanced Course as Design and Development of Embedded Systems using ARM Processors.
Show cases plenty of plat forms, based on many ARM venders and Sensors both Hardware and Software support.
Syllabus:
Familiarization to Open-Source Embedded Systems, ARM Architecture understanding the Evaluation kits both
Hardware, Software, Development Platforms like NXP, SILABS, STMICRO etc. Introduction to many IoT
Sensors, interfaces to ADC/DAC etc. Introduction to RF Modules (BT, LORANAN, WiFi, GPS etc).
Demonstration of IoT based projects.
Target Group:
Hardware Electronic Engineers.
Faculty:
Mr. M Krishna Kumar
(Retd.),
PRS., Dept. of ESE
(CEDT),
IISc.., Bengaluru
Email. [email protected]
Faculty:
Mr. S M
Narasimhan
Electronic
Design
Consultant,
Mysuru
Faculty:
Dr. Arulalan Rajan,
Formerly,
Assistant Prof.,
Dept. of E&C Engg.,
NITK, Surathkal..
Email:
m
Reference Books:
1. Jonathan W. Valvano
Embedded Systems: Real-time Interfacing to
ARM Cortex-M Microcontrollers.
Volume 2 Fourth Edition, July 2014.
2. Joseph Yiu
System-on-Chip Design with Arm Cortex-M
Processors.
3. Steve Furber
Arm System-On-Chip Architecture.
Who can apply?
BE/B.Tech/ AMIE or equivalent.
Pre- Requisites:
Basic Knowledge in Analog, Digital Electronics
and Microcontrollers.
Course Fee: Rs. 10,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s – 2.00 pm to 4.00 pm
Page 33 of 36
25. Online Course on Foundations of Intellectual Property Rights (IPRs) –
Legal and policy perspective (2:0)
Objectives:
The aim of this course is to introduce participants to the broad legal and policy perspective of Intellectual
Property Rights (IPRs) with a specific focus on the Indian IPR regime. Through the course, participants will
learn about different aspects of IP, their importance for business and society, and their protection/ enforcement
(in India and worldwide) through practical case examples. After completing this course, participants will be able
to understand different types of IP rights, appreciate the benefits and challenges, and understand the aspects of
enforcement in India and abroad
Syllabus:
Foundation of intellectual property – Historical reference, categorization & characteristics international
perspective; Patents – Introduction, economic impact, Patent Cooperation Treaty (PCT), benefits and
enforcement of patent rights; Copyrights – Rights protected by copyright, acquisition and transfer of copyright,
India; Trademarks –characteristics and protection of TM WIPO’s Madrid System and other treaties;
Geographical Indication (GI) – international convention and treaties, GI vs. TM, Laws of GI in India; Industrial
Design (ID) –Laws to protect ID in India, Worldwide protection of ID – The Hague system of WIPO, Case
examples and discussion; Civil society, public policy and IP – Social dimension of IP, IPR and public policy
(technology transfer on fair, reasonable and equitable terms, access of medicines and environment-friendly
technologies)
Target Group:
R&D organization or research labs, Research and teaching professionals, Entrepreneurs or small business owners,
Government officers
Faculty:
Prof. Anjula Gurtoo
Dept of MS,
IISc, Bengaluru
Email: [email protected]
Faculty: Dr Akriti Jain
Post doc,
Dept of MS, IISc,
Bengaluru
Email: [email protected]
Reference Books:
1. Reddy P, Chandrashekaran S. Create, copy, disrupt.
India's intellectual property dilemmas. Oxford
University Press India; 2017.
2. Halliburton M. India and the Patent Wars. Cornell
University Press; 2017 Nov 15.
3. Ahuja, V. K. Intellectual Property Rights in India.
LexisNexis Butterworths Wadhwa Nagpur; 2009
Who can apply?
Master’s degree in any discipline
Course Fee: Rs. 10,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Saturday’s – 2.00 pm. to 4.00 pm
Page 34 of 36
26.Online Course on Machine Learning (3:0)
Objectives:
To introduce the registrants to both the Theoretical and Practical aspects of Machine Learning. This will be
useful to both Practitioners and early Researchers and Academicians.
To introduce the registrants to the both the Theoretical and Practical aspects of Machine Learning. This will be
useful to both Practitioners and early Researchers and Academicians.
Syllabus:
Introduction to Machine Learning (ML). Representation and Search. Learning Paradigms: Rote Learning, Learning by
Deduction, Learning by Analogy, Learning by Abduction, Learning by Induction.
Foundations of ML: Role of Linear Algebra in ML; Information Theory in ML, Logic in ML, Probability in ML; and
Graphs in ML.
Inductive Learning: Clustering, Supervised and Semi-Supervised Learning, Knowledge-Based Learning.
Deep Learning: Convolution Neural Nets, Recurrent Neural Nets.
Applications: Information Retrieval and Network Embedding in Social and Information Networks.
Target Group:
Industry/R&D Units/ Academic Institutions interested in Machine Learning and Artificial Intelligence.
Faculty:
Prof. M Narasimha Murty
Honorary Professor,
Dept. of CSA.,
IISc, Bengaluru.
Email: [email protected]
Reference Books:
1. Tom Mitchell,
Machine Learning, Indian Edition, July 2017.
2. M N Murty and V Susheela Devi,
Introduction to Pattern Recognition and
Machine Learning, IISc Lecture Series Notes,
World-Scientific, IISc Press, 2015.
3. S. J. Russell and P. Norvig,
Artificial Intelligence: A Modern Approach,
Pearson, Delhi, 2016.
Who can apply?
BE/BTech in any branch of Engineering or MSc in
Mathematics/Physics/Statistics/Computer Science or
MCA.
Pre-requisites:
A good background of college level mathematics and
programming.
Course Fee: Rs. 15,000/- + 18% GST
Online Seats are Limited to 100
Online Classes using Microsoft Teams
Schedule: Sunday’s – 10.00 am. to 1.00 pm
Page 35 of 36
Appendix ‘A’ PROFORMA
NAME OF THE COLLEGE
PROVISIONAL CERTIFICATE
This is to certify that Sri/ Smt. ………………………………. was a student of this college
studying in ……………………………*
Course ……………………………………………………….**
Branch during the Session …………………. to ……………………………………..…
He / She have Successfully Completed the course as prescribed by the ……….………...
…………………………………………………………………………………………......
University with regard to course of study, attendance, sessional requirements etc.
He / She has passed the final ……………………………..* examination held during
……………………… securing …………………..class as per the results announced by
the University. He / She will be awarded the …………………………. …..degree during
the next convocation of the university.
College Seal
Date: PRINCIPAL
*Appropriate course to be filled in (B.E., B.Tech., M.E., M.Tech., M.Sc., and M.Com.
MBBS. Etc.)
**Mention Civil, Electrical, Electronics, Chemistry, Biology, Etc.
Page 36 of 36
IMPORTANT DATES
Apply online on CCE portal
17th June 2021
Thursday
Receipts of application along
with fees (upto)
From
To
17th June 2021
25th July 2021
Thursday
Sunday
Classes Commence
From 02nd August 2021 Monday
Final Exams From 29th November 2021 Monday
To 04th December 2021 Saturday
CCE-PROFICIENCE
Coordinator, Indian Institute of Science,
Bangalore - 560 012
Phone: + 91 080 22932508
E-mail: [email protected]
URL: www.cce.iisc.ac.in/proficience
Working Hours: Monday through Friday: 09.30 hrs. to 18.00 hrs.
Saturdays’: 10.00 hrs. to 16.00 hrs.