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

Speech recognition

http://youtu.be/Nu-nlQqFCKg?t=7m30s

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

Office Hours

Source: PhD Comics Movie 2

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