Post on 20-May-2020
Introduction to Machine Learning
Jia-Bin Huang
Virginia Tech Spring 2019ECE-5424G / CS-5824
Today’s class
• Introduction• A little about us
• A little about you
• Machine learning• What is machine learning?
• Types of machine learning
• Example applications
• Course logistics
About me• Born and raised in Taiwan
National Chiao-Tung UniversityB.S. in EE
UIUCPh.D. in ECE 2016
Microsoft ResearchResearch Intern
Disney ResearchResearch Intern
National Chiao-Tung UniversityB.S. in EE
UIUCPh.D. in ECE 2016
Microsoft ResearchResearch Intern
Disney ResearchResearch Intern
Image Completion [SIGGRAPH14]
- Revealing unseen pixels
Video Completion [SIGGRAPH Asia16]
- Revealing temporally coherent pixels
Facebook F8 Keynote Talk 2017 Adobe Max 2017
Image super-resolution [CVPR15]
- Revealing unseen high frequency details
Detecting migrating birds [CVPR16]
Object tracking [ICCV15]
Multi-face tracking [ECCV16]
Visual Tracking- Locating moving objects across video frames
Weakly supervised localization [CVPR16] Unsupervised feature learning [ICCV17]
Learning with weak labels
Teaching Assistant: Chen Gao
• 1st year PhD student in ECE, VT
• Email: chengao@vt.edu
• Web: https://gaochen315.github.io/
• Office hour: • TBD
• Research:
Teaching Assistant: Shih-Yang Su
• 1st year PhD student in ECE, VT
• Email: chengao@vt.edu
• Web: https://lemonatsu.github.io/
• Office hour: • TBD
• Research:
A little about you
• Find two persons near you
• Introduce yourself• Name?
• Department?
• Why taking this class?
• One interesting fact?
• Introduce your neighbors to the class!
What this course is about?
Learning to Teach Machine to Learn
Let’s chat!
• What is machine learning?
• What applications?
Discuss with your neighbor
What is machine learning?
• Field of study that gives computers the ability to learn without being explicitly programmed
Arthur Samuel (1959)
What is machine learning?
•A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
Tom Mitchell (1998)
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
Designing a spam filter
o Classifying emails as spam or not spam
o Watching you label emails as spam or not spam
o The number (or fraction) of emails correctly classified as spam/not spam
Slide credit: Andrew Ng
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
Designing a spam filter
o Classifying emails as spam or not spam
Tasks T
o Watching you label emails as spam or not spam
Experience E
o The number (or fraction) of emails correctly classified as spam/not spam
Performance measure P
Slide credit: Andrew Ng
Types of machine learning algorithms
• Supervised learning• Training data includes desired outputs
• Unsupervised learning• Training data does not include desired outputs
• Weakly or Semi-supervised learning• Training data includes a few desired outputs
• Reinforcement learning• Rewards from sequence of actions
Slide credit: Dhruv Batra
Machine learning algorithms
Supervised Learning
Unsupervised Learning
Discrete Classification Clustering
Continuous RegressionDimensionality
reduction
Machine learning algorithms
Supervised Learning
Unsupervised Learning
Discrete Classification Clustering
Continuous RegressionDimensionality
reduction
Breast cancer (malignant, benign)
Malignant?
0 (No)
1 (Yes)
Tumor Size
Classification problemDiscrete valued outpute.g., 0 or 1
Multi-class classificatione.g., 0 or 1 or 2 or 3
Tumor Size
Slide credit: Andrew Ng
Multiple features
• Clump thickness
• Uniformity of cell size
• Uniformity of cell shape
• …
Tumor Size
Age
?
Slide credit: Andrew Ng
Image classification
Spotting eye disease
• Recognize 50 sight-threatening eye diseases
• As accurately as world-leading expert doctors
Clinically applicable deep learning for diagnosis and referral in retinal disease, Nature Medicine, 2018
https://www.youtube.com/watch?v=MCI0xEGvHx8
Face recognition
Facebook auto-tagging
Machine Translation
https://www.youtube.com/watch?v=WeByuOD8k1c
Speech Recognition
Slide Credit: Carlos Guestrin
Predicting aftershock patterns
Deep learning of aftershock patterns following large earthquakes, Nature, 2018
Credit: Aflo/REX/Shutterstock
Machine learning algorithms
Supervised Learning
Unsupervised Learning
Discrete Classification Clustering
Continuous RegressionDimensionality
reduction
Housing price prediction
Price ($)in 1000’s
500 1000 1500 2000 2500
100
200
300
400
Regression problemContinuous valued output (price)
Size in feet^2
Slide credit: Andrew Ng
Stock market
Slide credit: Dhruv Batra
Weather prediction
Temperature
Slide credit: Carlos Guestrin
Human pose estimation
DensePose, CVPR 2018
Facial landmark alignment
Snapchat filterhttps://www.youtube.com/watch?v=Pc2aJxnmzh0
Machine learning algorithms
Supervised Learning
Unsupervised Learning
Discrete Classification Clustering
Continuous RegressionDimensionality
reduction
Supervised Learning
𝑥1
𝑥2
𝑥1
𝑥2
Unsupervised Learning
Google news
Clustering DNA microarray data
build groups of genes with related expression patterns (also known as coexpressed genes)
Source: Su-In Lee et al.
Slide credit: Andrew Ng
Machine learning algorithms
Supervised Learning
Unsupervised Learning
Discrete Classification Clustering
Continuous RegressionDimensionality
reduction
Dimensionality reduction
𝑥1
𝑥2
3D face modeling
A morphable model for the synthesis of 3D faces, SIGGRAPH 1999
Shape modeling
SMPL: Skinned multi-person linear model, SIGGRAPH Asia 2015
Cocktail party problem
Source: https://hbr.org/2016/11/the-competitive-landscape-for-machine-intelligence
Course Overview
General information
• Course title: Advanced Machine Learning• Not really… this is an introductory machine learning course
• ECE-5424 / CS-5824• Mon and Wed 2:30 PM – 3:45 PM
• Surge Space Building 118C
• Office hours - Jia-Bin• Mon 3:45 – 4:45 PM
• Office hours - Chen, Shih-Yang• TBD. Survey on Piazza/Canvas
Useful links
• Course webpage: http://bit.ly/vt-machine-learning-spring-2019• Download lecture slides
• Piazza discussion forum: https://piazza.com/class/jr6vbmqyvwy3wk• All communications go through piazza. No emails please.
• HW submission: https://canvas.vt.edu/• Start early!
• Anonymous course feedback: https://goo.gl/forms/nSz66NogxKXnXLBD2
Textbooks (optional)
Course work
• Homework assignments (50%)• Six main homework assignments + HW0• Late policy: Up to six free late days. After that, a penalty of 10% per day.
• Midterm exam (10%)
• Final exam (15%)
• Final project (25%)• Proposal, project report, and spotlight video• Work in a team of 2-3 students
Grading[0-60] F, [60-62] D-, [63-66] D, [67-69] D+, [70-72] C-, [73-76] C, [77-79] C+, [80-82] B-, [83-86] B, [87-89] B+, [90-92] A-, [93-100] A
Request
• Homework extension request• Only for medical/family emergency (please send me email with doctor’s note)
• No “I have an interview this week”, “I have a midterm exam”, “I am busy recently.”
• Homework regrade request• One week after the grade release date
• Final grading change request• No “I need to get an B+ to graduate”, “Can I can a grade upgrade?”
Academic Integrity
• Can discuss HW with peers, but cannot copy and/or share code
• Carefully document any sources within HW hand-in
• Do not use code from Internet unless you have permission• If you’re not sure, ask
• Do not use your published work as your final project
• Plagiarism. Zero tolerance. We are required to report it to the university.
Course enrollment
• Classroom capacity 140• (70 ECE session + 70 CS session)
• A long waiting list• Drop the class if you are not able to commit your time
• Policy: no force-add students to a full class.
• Sit in• Please leave room for students who registered the class
Prerequisites
• Linear algebra, basic calculus• Review: http://cs229.stanford.edu/section/cs229-linalg.pdf
• Probability and statistics• Review: https://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf
• Python (NumPy)• http://web.stanford.edu/class/cs224n/readings/python-review.pdf
• Review: Python review session by TAs
Course topics
• Supervised learning• Linear regression, logistic regression, SVM, deep neural network, ensemble
methods
• Unsupervised learning• K-means, PCA, EM, GMM
• Anomaly detection, recommender systems
• Generative models, sequence predictions, reinforcement learning
What to expect from this course
• Broad coverage • Focus is on the fundamental, rather than specific systems.
• Not about teaching you to use toolbox
• Background to delve deeper into any machine learning related topics
• Practical experience
• Lots of work, tough material, fast pace, but lots of learning too!
Other related courses at Virginia Tech
• Introductory courses:
• Introduction to Machine Learning
• Introduction to Artificial Intelligence
• Computer Graphics
• Advanced courses:• Deep Learning
• Probabilistic Graphical Models and Large-Scale Learning
• Advanced Computer Vision
• Fundamentals:
• ECE 5734 Convex Optimization
• STAT 5444 Bayesian Statistics
• STAT 4714 Prob and Stat for EE
Goals and Expectations
• My goal: • maximize the learning effectiveness of your time
• What I expect from you• Attend and participate, when possible
• No screens please (tablet, phone, laptop, etc)
• Start assignments well before deadline
• Tell me what’s working and suggest improvements Anonymous feedback form
Things to remember
• Machine learning is awesome!
• To-Do• Check out the review material
(linear algebra, probability, Python)
• Start working on HW 0
• Next class: k-NN classifier
• Questions?
Supervised Learning
Unsupervised Learning
Discrete Classification Clustering
Continuous
RegressionDimensionality
reduction