CS 445/545
Winter Quarter 2019
Time : Tuesdays and Thursdays, 2:003:50pm
Location: Cramer Hall 71
Instructor:
Melanie Mitchell,
FAB 11513, (503) 7252412, mm@pdx.edu
Office hours: T,Th 10am11am, or by appointment.
Teaching Assistant:
LiYun Wang liyuwang@pdx.edu
Office hours: M,W 4pm5pm in CS Fishbowl.
Course Mailing List: mlwinter2019@cs.pdx.edu
Prerequisites: Undergraduatelevel courses in calculus and linear algebra. Facility in at least one highlevel programming language.
Main topics:: Perceptrons, neural networks, logistic regression, evaluating classifiers, support vector machines, ensemble learning, Bayesian learning, unsupervised learning, deep learning, and reinforcement learning.
Textbook: No textbook. Readings will be assigned from materials available online.
Relation to CS 441/541 (Artificial Intelligence): A couple of the same topics will be covered (e.g., neural networks), but these will be covered in more depth and at a more theoretical level in this course than in CS 441/541. Otherwise, the topics in this course differ from the topics covered in CS 441/541.
Homework: The class will have several homework assignments, involving writing code for and/or experimenting with various machine learning methods.
Late homework policy: Students must request and be granted an extension on any homework assigment before the assignment is due. Otherwise, 5% of the assignment grade will be subtracted for each day the homework is late, up to a maximum of 25%.
Quizzes: The class will have several short inclass quizzes to test basic understanding of the material presented in class and in the readings. You are allowed to bring in one doublesided page of notes for each quiz. There will be no midterm or final exam.
Final Project: Students will work in small teams (14 people) on a final project. Details will be given during class.
Grading: Homework 50%, Quizzes 20%, Final project 30%.
Academic integrity: Students will be responsible for following the PSU Student Conduct Code.
Students with disabilities: If you are a student with a disability in need of academic accommodations, you should register with the Disability Resource Center and notify the instructor immediately to arrange for support services.
Syllabus (subject to change):
Date 
Topics 
Homework and Reading 
Tuesday January 8 
Introduction to machine learning (pptx or pdf) Class "To Do" list 
Reading: Homework 1 (Perceptrons), due Thursday January 17: Assignment, mnist_train.csv, mnist_test.csv 
Thursday January 10 
Perceptrons, continued Perceptrons Exercises solutions  
Tuesday January 15 
Multiclass classification (pptx or pdf) Multilayer neural networks (pptx or pdf) Review for Quiz 1 

Thursday January 17 
Quiz 1 Multilayer neural networks, continued 
Homework 2 (Neural Networks), due Tuesday January 29: Assignment 
Tuesday January 22 
Support vector machines 
Reading: A. BenHur and J. Weston, A User's Guide to Support Vector Machines 
Thursday January 24 
Support vector machines, continued Dimensionality reduction 

Tuesday January 29 
Feature selection Review for Quiz 2 
Reading:: T. Fawcett, An introduction to ROC analysis, Sections 14, 7 Homework 3 (SVMs and feature selection), due Thursday February 7 
Thursday January 31 
Quiz 2 Final project instructionsEvaluating classifiers, continued 

Tuesday February 5 
Bayesian learning 
Reading: T. Mitchell, Chapter on Naive Bayes and Logistic Regression 
Thursday February 7 
Bayesian learning, continued Logistic regression Review for Quiz 3 
Homework 4 (Bayesian learning and logistic regression), due Tuesday February 19 
Tuesday February 12 
Quiz 3 Unsupervised learning, Part I: KMeans Clustering 
Reading: Chapter 8 from Introduction to Data Mining by Tan, Steinbach, and Kumar, pp. 487515, 532540, 546550. 
Thursday February 14 
Unsupervised learning, Part II: Gaussian Mixture Models Onepage project proposal due 

Tuesday February 19 
Review for Quiz 4 Reinforcement learning 
Reading: pp. 278280 of Reinforcement Learning for True Adaptive Traffic Signal Control (a brief and clear introduction to Qlearning) Homework 5 (Kmeans clustering), due Thursday February 28 
Thursday February 21 
Quiz 4 Reinforcement learning, continued 

Tuesday February 26 
Ensemble learning 
Reading: R. Schapire, A brief introduction to boosting 
Thursday February 28 
Review for Quiz 5 Deep learning for vision 

Tuesday March 5 
Quiz 5 Deep learning for vision, continued 

Thursday March 7 
Deep learning for language  
Tuesday March 12 
Deep learning for language, continued
Common Sense in AI 

Thursday March 14 
Generative Adversarial Networks 

Friday March 22 
Final projects due 
