BS6207 2021 Lee Hwee Kuan
Transcript of BS6207 2021 Lee Hwee Kuan
Welcome to BS6207
2021 Lee Hwee Kuan
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An Innovative Way to Learn and Teach Deep Learning ✤ We all learn together - sharing sessions
✤ Students are expected to learn most techniques online by themselves
✤ What is the use of this course? ๏ This course provides concepts and understandings that cannot be easily
self-learned ๏ This course provides the real life experience for doing deep learning ๏ This course teaches you how to think in different ways
✤ Always bring your laptop, we do coding in class
✤ If you want to get my attention, call me by my first name “Hwee Kuan”, you can call me “Dr Lee” or “Prof Lee” if you want me to ignore you
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Sharing sessions
If we categorize people into those with technical skills and those who can explain their ideas, there are 4 combinations.1.Those who have no skills & cannot explain their ideas2.Those who have skills & cannot explain their ideas3.Those who have no skills & can explain their ideas4.Those who have skills & can explain their ideas
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Sharing sessions
1.Those who have no skill & cannot explain their ideas These people may be beggars
2.Those who have skills & cannot explain their ideas These people are good servants
3.Those who have no skill & can explain their ideas These people are the bosses
4.Those who have skills & can explain their ideas These people are the masters
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Sharing sessions
There are 8 registered students.
We aim to have one in four students to present something in front of the class.
Presentations will be on: 1.Assignment solutions 2.Project solutions
Some presentation skill coaching will be provided by the instructors
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I teach the whole package
✤ Communication skills✤ Teamwork✤ Decision making and scientific methods✤ Technical skills
How our world is going to change?
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How our world is going to change?
Old New
Driving Work from home
Manufacturing Shop from home
Clerical work Self healthcare
Retail Self education
Mass education AI work
Maids & cleaners
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How our world is going to change?
http://www.mrbrown.com/blog/2011/03/maid-for-the-army-2.html
Boston Dynamics 2017Storm 2011
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What’s Deep Learning for?
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Deep Learning related work in Robotics
https://www.youtube.com/watch?v=EP_NCB3KkiY https://www.youtube.com/watch?v=rVlhMGQgDkY https://www.youtube.com/watch?v=M8YjvHYbZ9w
Check out:Boston Dynamics
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Deep Learning related work in Financial Technologies (Fintech)
“. . .fintech is on the verge of a truly revolutionary moment with the integration of artificial intelligence and deep learning into financial services.
http://fintechnews.sg/8840/fintech/deep-learning-finance-summit-comes-singapore-discuss-emerging-trends-opportunities/
Using machine learning for insurance pricing optimization
https://cloud.google.com/blog/big-data/2017/03/using-machine-learning-for-insurance-pricing-optimization
Much trading is done by machines especially high frequency trading
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Deep Learning related work in Retail
Amazon Gono cashier, no queues shopping
https://venturebeat.com/2016/12/05/amazon-launches-amazon-go-a-brick-and-mortar-grocery-store-that-does-away-with-checkouts/
check this out!https://www.amazon.com/b?
node=1600858901113
“Legal Robot uses machine learning techniques like deep learning to understand legal language, . . . “
https://www.legalrobot.com/
Deep Learning related work in Legal
“. . . the UK-based news resource, LegalFutures, predicts that technologies automating the work of associates, herald the collapse of law in less than 15 years
A recent study by McKinsey & Co estimates that 23% of lawyer time is automatable. Similar research by Frank Levy at MIT and Dana Remus at University of North
Carolina School of Law concludes that just 13% of lawyer time can be performed by computers. . .”
https://blogs.thomsonreuters.com/answerson/artificial-intelligence-legal-practice/
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Deep Learning related work in Manufacturing
Foxconn replaces '60,000 factory workers with robots'
http://www.bbc.com/news/technology-36376966
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Deep Learning related work in Farming
Farmers are getting help from researchers and scientists who have turned the keen eye of AI toward agriculture, using deep learning
applications to not only predict crop outputs but also to monitor water levels around the world and help detect crop diseases before one
spreads.
https://dataskeptic.com/blog/news/2017/deep-learning-is-driving-the-new-agriculture-revolution
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Deep Learning related work in Biomedical
Deep Learning Drops Error Rate for Breast Cancer Diagnoses by 85% https://blogs.nvidia.com/blog/2016/09/19/deep-learning-breast-cancer-diagnosis/
Google researchers trained an algorithm to recognize a common form of eye disease as well as many experts can.
https://www.technologyreview.com/s/602958/an-ai-ophthalmologist-shows-how-machine-learning-may-transform-medicine/
Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
G. Litjens et al Scientific Reports 6, 26286 (2016)
Dermatologist-level classification of skin cancer with deep neural networksA. Esteva Nature 542, 115-118 (2017)
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The Next Era: Deep Learning in Pharmaceutical Research.
S. Ekins
Pharm Res. 2016 Nov;33(11):2594-603. doi: 10.1007/s11095-016-2029-7. Epub 2016 Sep 6.
Deep Learning related work in Pharmaceutical
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Many other Deep Learning related work
Smart City Transportation
Aviation Telecommunications
Construction Education
. . . think of anything XXX, then search for
“deep learning XXX”
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Historical notes on
neural networks and deep learning
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1943Warren McCulloch (neurophysiologist), Walter Pitts (mathematician) Mathematical model of the brain McCulloch, Warren; Walter Pitts (1943). "A Logical Calculus of Ideas Immanent in Nervous Activity". Bulletin of Mathematical Biophysics. 5 (4): 115–133.
1949 Donald O. Hebb Strengthening of connection between neurons Hebb, D. O. (1949). The Organization of Behavior: A Neuropsychological Theory. New York: Wiley and Sons.
1959Bernard Widrow, Marcian Hoff Single layer and multilayer neural nets. ADALINE and MADALINE An adaptive "ADALINE" neuron using chemical "memistors"
1970Seppo Linnainmaa While gradient descend algorithm dates back much earlier, Seppo contributed to the modern idea of back propagation Linnainmaa, Seppo (1970). The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master's Thesis (in Finnish), Univ. Helsinki, 6-7.
1989George Cybenko Universal approximation theorem, sigmoid function Cybenko, G. (1989) "Approximations by superpositions of sigmoidal functions", Mathematics of Control, Signals, and Systems, 2 (4), 303-314
1991 Kurt Hornik Universal approximation theorem, more general function Kurt Hornik (1991) "Approximation Capabilities of Multilayer Feedforward Networks", Neural Networks, 4(2), 251–257
https://cs.stanford.edu/people/eroberts/courses/soco/projects/neural-networks/History/history1.html
Historical notes
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‘Contemporary’ history of neural nets
1974 Paul Werbos, Backpropagation
1980 Kunihiko Fukushima, Neocogitron which inspired Convolutional Neural Networks
1985 Hilton & Sejnowski, Boltzmann Machine
1986 Paul Smolensky, Harmonium, later known as Restricted Boltzmann Machine Michael I. Jordan Recurrent Neural Network
1990 Yann LeCun, LeNet - convolutional neural net
2006 G. Hinton, Deep Belieft Net, layer wise pretraining
2009 Salakhutdinov & Hinton, Deep Boltzmann Machines
2012 N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdin, Dropout
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‘Contemporary’ history of neural nets
2014 Ian Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative Adversarial Networks
2015 Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Network
2015 Nicolas Papernot et al, Adversarial Deep Learning
2016 Shaoqing Ren et al, Region Proposal Network
2017 David Silver et al, Alpha-Go Zero
2018 SMA Eslami et al, Generative Query Network
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https://www.youtube.com/watch?v=RBJFngN33Qo
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Generative Query Networks
23684184 x 4729472 =Our world versus computer world
112013685070848
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Our world versus computer world
Life Science
Healthcare
Relationship
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What makes Deep Leaning so good?
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can this thing think?
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can they think?
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can they think?
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http://img.webmd.com/dtmcms/live/webmd/consumer_assets/site_images/articles/image_article_collections/anatomy_pages/
brain2.jpg?resize=646px:*&output-quality=100
can it think?
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can this thing think?
w1
w2
w3 a = w1 x1 + w2 x2 + w3 x3
y = a if a > 0, else y = 0x3
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With millions of connections, it started to “think”
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How do we get from molecules (proteins) to celland then to life?
proteins
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How do we get from molecules (proteins) to celland then to life?
http://learn.genetics.utah.edu/content/stemcells/quickref/somaticstemcells.jpg
proteins
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https://www.pinterest.com/sattele/termite-mound/ http://img.ev.mu/images/reportages/188/520x342/09.jpg
Life exhibits complexity
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How extreme can life become?How extreme can emergent behaviour become?
very extreme indeed
some life forms on earth evolved the ability to control the
behaviour of another life form!
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Ophiocordyceps sinensis冬⾍草
S$10-50 per piece
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http://microbewiki.kenyon.edu/index.php/Ophiocordyceps_sinensis
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The foraging carpenter ant walks through an area of rainforest floor infested with microscopic spores dropped by a mature fungus. The spore excretes an enzyme that eats through the ant’s exterior shell.
After two days, the ant leaves its tree colony and climbs down to a spot where humidity and temperature are optimal for the fungus to grow. The ant crawls onto a stem or the underside of a leaf and bites into its main middle vein so it won’t fall. Then it dies.
The fungus consumes the ant’s internal organs, using its shell as a protective casing. The fungus’ main stem, called a stroma, erupts from the back of the ant’s head and grows
The mature fungus releases spores from its stroma. The spores fall to the ground creating a 10-square-feet “killing zone” which will attack new ants.
https://microbewiki.kenyon.edu/index.php/Ophiocordyceps_unilateralis
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https://www.youtube.com/watch?v=XuKjBIBBAL8
Cordyceps
How extreme can machines become?
are we able to make a machine that is as complex as the most
primitive life known to us?
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How to learn Deep Learning
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If you only know how to drive, you can go far
If you have money and get a sport car, you can go far very fast
You don’t have to know how to build a car
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“Deep Learning” is free and they go fast
using them is as easy as driving a car
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But there is a problem!!
…even if you have the data!
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To go to great places, you need to know where to go
No point going fast but go in circles you need to know the route
You need to know if your car is ok or is breaking
The computer always give you
an outputis it correct?
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Different levels of understanding Deep Learning
There are those who do not know what they are doing. Their computational results are unreasonable
There are those who know how to get some good results but cannot explain them
There are those who understand what is going on with their experiments. Able to explain their results
There are those who can combine different methods to create new things in Deep Learning
There are those who can fundamentally change Deep Learning research
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Very basic
lets get everyone on the same level sorry if this seems too simple to some of you
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input output
fuzzy incomplete
input
fuzzy incomplete
output
only precise input
011000111001100. . .
only precise output
010
Machine
Human
Human
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Lets play a game. . .
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i like this picture
i like this picture
i like this picturei like this picture
i don’t like this picture
i don’t like this picture
i like this picture
i don’t like this picture i don’t like this picture
i like this picture
i don’t like this picture
like or dislike?(a)
like or dislike?(b)
like or dislike?(c)
like or dislike?(d)
Testing set
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Like
Dislike
Training set
like or dislike?(a)
like or dislike?(b)
like or dislike?(c)
like or dislike?(d)
Testing set
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Like
Dislike
Training set
Answer: Like(a)
Answer: Dislike(b)
Answer: Dislike(c)
Answer: Like(d)
Testing set
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Age Prostate Specific Antigen(a blood test reading) Got Prostate Cancer
59 4.9 ng/mL Yes72 3.9 ng/mL Yes45 6.0 ng/mL Yes47 3.2 ng/mL No39 3.9 ng/mL No89 3.5 ng/mL Yes61 5.5 ng/mL Yes62 2.1 ng/mL No49 3.4 ng/mL No95 3.1 ng/mL Yes67 4.3 ng/mL Yes49 3.8 ng/mL ?58 4.3 ng/mL ?88 4.1 ng/mL ?31 2.1 ng/mL ?
Case of prostate cancer diagnosis
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Age Prostate Specific Antigen(a blood test reading) Got Prostate Cancer
45 6.0 ng/mL Yes61 5.5 ng/mL Yes59 4.9 ng/mL Yes67 4.3 ng/mL Yes72 3.9 ng/mL Yes89 3.5 ng/mL Yes95 3.1 ng/mL Yes
39 3.9 ng/mL No49 3.4 ng/mL No47 3.2 ng/mL No62 2.1 ng/mL No49 3.8 ng/mL ?58 4.3 ng/mL ?88 4.1 ng/mL ?31 2.1 ng/mL ?
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Age Prostate Specific Antigen(a blood test reading) Got Prostate Cancer
45 6.0 ng/mL Yes61 5.5 ng/mL Yes59 4.9 ng/mL Yes67 4.3 ng/mL Yes72 3.9 ng/mL Yes89 3.5 ng/mL Yes95 3.1 ng/mL Yes
39 3.9 ng/mL No49 3.4 ng/mL No47 3.2 ng/mL No62 2.1 ng/mL No49 3.8 ng/mL No?58 4.3 ng/mL borderline?88 4.1 ng/mL Yes?31 2.1 ng/mL No?
prostate specific antigen (ng/mL)
age
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4.0 4.53.53.0
Prostate cancer prediction
prostate specific antigen (ng/mL)
age
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4.0 4.53.53.0
Prostate cancer prediction
Prostate cancerHealthy
prostate specific antigen (ng/mL)
age
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4.0 4.53.53.0
Prostate cancer prediction
Prostate cancerHealthy
prostate specific antigen (ng/mL)
age
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4.0 4.53.53.0
Prostate cancer prediction
Prostate cancerHealthy
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prostate specific antigen (ng/mL)
age
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4.0 4.53.53.0
Prostate cancer prediction
Prostate cancerHealthy ?
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prostate specific antigen (ng/mL)
age
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4.0 4.53.53.0
Prostate cancer prediction
Prostate cancerHealthy
Decision Boundaries
Supervised learning framework input space image,
blood pressure, text,
speech
input instance (x1,x2,. . .)
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black / white labels on
input
Supervised learning framework
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input: a point
in input space
output: black
or white
Machine
input space image,
blood pressure, text,
speech
Supervised learning framework
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The decision boundary
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The decision boundary
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The decision boundary
?
?
?
?
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The decision boundary
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Animation @ playground search for
“playground tensorflow"
introduction
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Different functionalities of applet
questionx1
x2
o
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answer
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questionx1
x2
o
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answer
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question
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answer
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question
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answer
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answer
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question
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answer
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answer
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answer
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answer - with sigmoid
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question
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Administrative Issues
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An Innovative Way to Learn and Teach Deep Learning ✤ We all learn together
✤ Students are expected to learn most techniques online by themselves
✤ Always bring your laptop, we do coding in class - a part of the time for lectures, the rest of the time for experiential learning
Communications mattersUse whatsapp for communications. This is the fastest way to get response.
For private matters you can privately WhatsApp me.
Get course materials from: https://web.bii.a-star.edu.sg/~leehk/index.html
I need one class monitor, who volunteer?
How do we want to do lecture? 2 hours on Fridays and 1 hr office 12-1pm on Wednesdays
Class monitor will set up a WhatsApp group for the whole class, including me in the group
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General grading and expectationsThere are those who do not know what they are doing. Their results are unreasonable
There are those who know how to get some good results but cannot explain them.
There are those who understand what is going on with their experiments. Able to explain their results.
There are those who know enough to combine different methods and be creative in using Deep Learning
There are those who break the frontiers of Deep Learning research.
F/E
D/C
B
A
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Assignments (10% * 5 = 50%)• For each assignment • It has multiple sub-tasks, including coding tasks • You need to submit the answers, code and execution results. Everything
in git hub, and one copy in NTULearn. • All assignment report must have: • Algorithm description in plain text, supplemented by equations if
needed (3-4 marks) • Code walk through, part-by-part of the code need to be explained
(2-3 marks) • Plots and results (1-2 marks) • Explanation and interpretation of results (3-4 marks)
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Final Project (50%)• Projects will be
• Submission deadline: pre-submission around week 7, final submission around week 10, to be confirmed
• Report deadline: around week 11, to be confirmed
• Assessment is based on the report (40%) and presentation (10%) • All students will present their work in class
• One student per project submission. You are allowed discuss project solutions among students.
• No copying of source code, code everything yourself!
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Final Project (50%)
Project details will be announced next week.
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Report format: Strictly limited to 6 pages (figures and text font size >10) + 1 page reference, any longer report will be rejected
Report template (40%)
• Problem definition
• Highlights (new algorithms, insights from the experiments)
• Dataset pre-processing description
• Training and testing procedure
• Experimental study
• (clarity, model understanding, and highlights are important for the assessment)
• Presentation -> score (10%)
Start your project early 97
*Do not* miss the deadline
30% grade deductions for within 1 week late submissions60% grade deductions for within 2 weeks late submissions
no grade given for beyond 2 weeks late submissions
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To be fair to everyone, request for late submission without grade deductions for emergency cases are to be done before week 9, otherwise request will not be entertained