CS480/680: Intro to MLLecture 00: Introduction

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Outl ine

• Course Logistics

• Course Overview

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Outl ine

• Course Logistics

• Course Overview

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Course Info

• Instructor: Yao-Liang Yu (yaoliang.yu@uwaterloo.ca)• Office hours: DC 3617, Th 11:30 – 12:30

• TAs: Amir Farrag, Jingjing Wang, Theo Hu

• Website: cs.uwaterloo.ca/~y328yu/mycourses/480

• slides, notes, policy, etc.

• Piazza: piazza.com/uwaterloo.ca/fall2019/cs480cs680/

• Announcements, questions, discussions, etc.

• Learn: https://learn.uwaterloo.ca• Assignments, solutions, grades, etc.

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https://piazza.com/uwaterloo.ca/fall2019/cs480cs680/homehttps://piazza.com/uwaterloo.ca/fall2017/cs489cs698/homehttps://learn.uwaterloo.ca/

Prerequisi tes

• CS341, STAT 206 or 231 or 241

• Basic probability, statistics, algorithms, linear algebra, calculus

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• Mathematical maturity

• Programming

https://julialang.org/https://www.python.org/

https://julialang.org/https://julialang.org/https://www.python.org/

What to expect

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Textbook

• No required textbook

• Lecture notes or slides will be posted on course web

• Some fine textbooks:

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Assignments

• 4 assignments with the following tentative plan:

• Submit on LEARN. Submit early and often.• Typeset using LaTeX is recommended

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Out date Due date CS480 CS680

A1 Sep. 05 Sep. 26 15% 15%

A2 Sep. 26 Oct. 22 15% 15%

A3 Oct. 22 Nov. 12 15% 15%

A4 Nov. 12 Dec. 03 15% 15%

Proposal Oct. 31 5% 5%

Report Dec. 15 20% 20%

https://en.wikipedia.org/wiki/LaTeX

Exam

• Midterm: 15%, Oct 22, in class

• Final exam: 25%, date TBA• Open book

• No electronics

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Policy

• Do your own work independently and individually• Discussion is fine, but no sharing of text or code

• Explicitly acknowledge any source that helped you

• Ignorance is no excuse!• Good online discussion, more on course website

• Serious offence will result in expulsion…

• NO late submissions!• Except hospitalization, family urgency, …

• Appeal within two weeks, otherwise final

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http://www.math.uwaterloo.ca/navigation/Current/cheating_policy.shtmlhttps://cs.uwaterloo.ca/~y328yu/mycourses/489/policy.html

Project

• Optional: can trade for final. Need TA / Instr. approval.

• You project should• Relate to machine learning (obviously)

• Allow you to learn something new (and hopefully significant)

• Be interesting and nontrivial (publishable)

• 2-4 pages proposal and

Enrol lment

• If you already enrolled• Good for you!

• If you are not enrolled yet• Permission numbers will be sent early next week

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Questions?

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Outl ine

• Course Logistics

• Course Overview

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What is Machine Learning (ML)?

• “Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.” --- Arthur Samuel (1959).

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• “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)

https://en.wikipedia.org/wiki/Arthur_Samuelhttp://www.cs.cmu.edu/~tom/

Why is ML important for YOU?

• First off, you use ML everyday

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• Lots of cool applications

• Excellent for job-hunting

Learning Categories

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Supervised

• Classification

• Regression

• Ranking

Reinforcement

• Control

• Pricing

• Gaming

Unsupervised

• Clustering

• Visualization

• Representation

Teacher provides answer Teacher provides motivation Surprise, surprise

Supervised learning

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Formally

• Given a training set of pairs of examples (xi, yi)

• Return a function f: X Y

• On an unseen test example x, output f(x)

• The goal is to do well on unseen test data• Usually do not care about performance on training data

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Reinforcement learning

• Not in this course…

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• CS486/686

Silver et al., Nature’16Abbeel et al., NIPS’06

Unsupervised learning

• Let the data speak for itself!

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Le et al., ICML’12

Focus of ML research

• Representation and Interpretation• How to represent the data? How to interpret result?

• Generalization• How well can we do on test data? On a different domain?

• Complexity• How much time and space?

• Efficiency• How many samples?

• Applications

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Course Overview

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Classic

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Neural Nets

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Generative Models

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Exotic

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Questions?

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