CS 445/545
Fall Quarter 2018
Time : Tuesdays and Thursdays, 10:0011:50am
Location: Engineering Building 93
Instructor:
Melanie Mitchell,
FAB 11513, (503) 7252412, mm@pdx.edu
Office hours: T,Th Noon1pm, or by appointment.
Teaching Assistant:
LiYun Wang liyuwang@pdx.edu
Office hours: M,W 3pm4pm in CS Fishbowl.
Course Mailing List: mlfall2018@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 (24 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 September 25 
Introduction to machine learning (pptx or pdf) Class "To Do" list 
Reading: Homework 1 (Perceptrons), due Thursday October 4 Assignment, mnist_train.csv, mnist_test.csv 
Thursday September 27 
Pretest solutions Perceptrons, continued  
Tuesday October 2 
Multilayer neural networks (pptx or pdf) Solutions to neural network exercises Review for Quiz 1 

Thursday October 4 
Quiz 1 Multilayer neural networks, continued 
Homework 2 (Neural Networks), due Tuesday October 16: Assignment 
Tuesday October 9 
Reading: A. BenHur and J. Weston, A User's Guide to Support Vector Machines  
Thursday October 11 
Support vector machines, continued 

Tuesday October 16 
Feature selection Evaluating classifiers (pptx or pdf) Review for Quiz 2 
Reading:: T. Fawcett, An introduction to ROC analysis, Sections 14, 7 Homework 3 (SVMs and feature selection), due Thursday October 25: Assignment 
Thursday October 18 
Quiz 2 Quiz 2 solutions Evaluating classifiers, continued 

Tuesday October 23 
Reading: T. Mitchell, Chapter on Naive Bayes and Logistic Regression 

Thursday October 25 
Bayesian learning, continued Solutions to "At home" exercises Solution to "In class' exercises, part 2, number 3 Logistic regression (pptx or pdf) Review for Quiz 3 
Homework 4 (Bayesian learning and logistic regression), due Tuesday November 6: Assignment 
Tuesday October 30 
Quiz 3 Quiz 3 solutions Unsupervised learning, Part I: KMeans Clustering (pptx or pdf) 
Reading: Chapter 8 from Introduction to Data Mining by Tan, Steinbach, and Kumar, pp. 487515, 532540, 546550. 
Thursday November 1 
Unsupervised learning, Part II: Gaussian Mixture Models (pptx or pdf) Onepage project proposal due 

Tuesday November 6 
Review for Quiz 4 Reinforcement learning (pptx or pdf) Solutions to inclass exercices, problems 1 and 2 
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 November 15: Assignment Optdigits dataset for Homework 5 
Thursday November 8 
Quiz 4 Quiz 4 solutions Reinforcement learning, continued 

Tuesday November 13 
Reading: R. Schapire, A brief introduction to boosting 

Thursday November 15 
Review for Quiz 5 Solutions to Exercises Deep learning for vision (pptx or pdf) (apologies for large files!) 

Tuesday November 20 
Quiz 5 Quiz 5 Solutions Deep learning for vision, continued 

Thursday November 22 
Thanksgiving holiday 

Tuesday November 27 
Deep learning for language (pptx or pdf)  
Thursday November 29 


Friday December 7 
Final projects due 
