Lecture 00: Introduction CS480/680: Intro to ML Lecture 00: Introduction 1/13/2019 Yao-Liang Yu

Outline Course Logistics Course Overview 1/13/2019 Yao-Liang Yu

Outline Course Logistics Course Overview 1/13/2019 Yao-Liang Yu

Course Info Instructor: Yao-Liang Yu (yaoliang.yu@uwaterloo.ca) Office hours: DC 3617, TTh 11:30 – 12:30 TAs: Hamidreza Shahidi (h24shahi), Jingjing Wang (jj27wang), KaiWen Wu (k77wu), Lian Xin (x9lian) Website: cs.uwaterloo.ca/~y328yu/mycourses/480 Syllabus, slides, notes, policy, etc. Piazza: piazza.com/uwaterloo.ca/fall2018/cs480cs680/ Announcements, questions, discussions, etc. Learn: https://learn.uwaterloo.ca Assignments, solutions, grades, etc. 1/13/2019 Yao-Liang Yu

Prerequisites Programming Mathematical maturity CS341, CS370/371 Basic probability, statistics, algorithms, linear algebra, calculus Mathematical maturity Programming 1/13/2019 Yao-Liang Yu

What to expect 1/13/2019 Yao-Liang Yu

Textbook No required textbook Lecture notes or slides will be posted on course web Some fine textbooks: 1/13/2019 Yao-Liang Yu

Assignments 4 assignments with the following tentative plan: Submit on LEARN. Submit early and often. Typeset using LaTeX is recommended Out date Due date CS480 CS680 A1 Sep. 11 Sep. 27 10% A2 Oct. 16 A3 Nov. 8 A4 Nov. 27 Proposal Oct. 23 5% Report Dec. 3 15% 30% 1/13/2019 Yao-Liang Yu

Exam Midterm: 20%, Oct 18, in class Final exam: 40%, date TBA Open book No electronics 1/13/2019 Yao-Liang Yu

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 1/13/2019 Yao-Liang Yu

Project Optional: 480/680 can trade for midterm/final, resp. You project should Relate to machine learning (obviously) Allow you to learn something new (and hopefully significant) Be interesting and nontrivial (publishable) 2-page proposal and <= 8 pages report 1/13/2019 Yao-Liang Yu

Grades 1/13/2019 Yao-Liang Yu

Enrollment If you already enrolled If you are not enrolled yet Good for you! Please take a look at the quiz and decide if you are comfortable with the background If you are not enrolled yet Please complete the quiz and hand in on next Tuesday Permission numbers will be based on that cs.uwaterloo.ca/~y328yu/mycourses/480/quiz.pdf 1/13/2019 Yao-Liang Yu

Questions? 1/13/2019 Yao-Liang Yu

Outline Course Logistics Course Overview 1/13/2019 Yao-Liang Yu

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). “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) 1/13/2019 Yao-Liang Yu

Why is ML important for YOU? First off, you use ML everyday Lots of cool applications Excellent for job-hunting 1/13/2019 Yao-Liang Yu

Learning Categories Supervised Reinforcement Unsupervised Classification Regression Ranking Reinforcement Control Pricing Gaming Unsupervised Clustering Visualization Representation Teacher provides answer Teacher provides motivation Surprise, surprise 1/13/2019 Yao-Liang Yu

How do humans learn? 1/13/2019 Yao-Liang Yu

Supervised learning 1/13/2019 Yao-Liang Yu

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 1/13/2019 Yao-Liang Yu

Reinforcement learning Not in this course… CS486/686 Silver et al., Nature’16 Abbeel et al., NIPS’06 1/13/2019 Yao-Liang Yu

Unsupervised learning Let the data speak for itself! Le et al., ICML’12 1/13/2019 Yao-Liang Yu

Focus of ML research Representation and Interpretation Generalization 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 1/13/2019 Yao-Liang Yu

Course Overview 1/13/2019 Yao-Liang Yu

Gimme, Gimme more Science special issue on AI Nature special issue on AI As always, google and wikipedia are your friends. 1/13/2019 Yao-Liang Yu

Questions? 1/13/2019 Yao-Liang Yu