CS 445/545
Winter Quarter 2017
Time : Tuesdays and Thursdays, 2:003:50pm
Location: University Pointe (PNT) 102
Instructor:
Melanie Mitchell,
FAB 11513, (503) 7252412, mmATpdx.edu
Office hours: T,Th Noon1pm, or by appointment.
Teaching Assistant:
Mike Lane, lane7ATpdx.edu
Office hours: M,W Noon1pm, FAB 115 in the "Data Lab" (across the hall from 11513).
Course Mailing List: ML2017@cecs.pdx.edu
Prerequisites: Undergraduatelevel courses in calculus, linear algebra, and probability and statistics. Facility in at least one highlevel programming language.
Main topics:: Perceptrons, neural networks, linear regression, logistic regression, evaluating classifiers, support vector machines, decision trees, ensemble learning, Bayesian learning, unsupervised learning, reinforcement learning, and deep 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, along with written exercises.
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.
Exams: The class will have an inclass final exam. There will also be 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, and four doublesided pages of notes for the final exam.
Grading: Homework 50%, Quizzes 20%, Final exam 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 Jan. 10 
Reading: Homework 1 (Perceptrons), due Tuesday Jan. 24: Assignment, mnist_train.csv, mnist_test.csv 

Thursday Jan. 12 
Snow day!  
Tuesday Jan. 17 
Snow day! 

Thursday Jan. 19 
Perceptrons, continued 

Tuesday Jan. 24 
Quiz 1 (30 min) Quiz 1 solutions 
Homework 2 (Neural Networks), due Thursday Feb 2: Assignment 
Thursday Jan. 26 
Multilayer NNs continued. 
Reading: A. BenHur and J. Weston, A User's Guide to Support Vector Machines 
Tuesday Jan. 31 
Support vector machines, continued 

Thursday Feb. 2 
SVMs, continued 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), due Tuesday Feb. 14: Assignment

Tuesday Feb. 7 
Quiz 2 (30 min) Quiz 2 solutions Evaluating classifiers, continued 

Thursday Feb. 9 
Reading: Tom Mitchell, Generative and discriminative classifiers: Naive Bayes and logistic regression, Sections 12. 

Tuesday Feb. 14 
Bayesian learning, continued Solutions to Exercises, Part 3 
Reading: X. Z. Fern, Notes on Logistic Regression Homework 4 (Bayesian learning and logistic regression), due Thursday Feb. 23: Assignment 
Thursday Feb. 16 
Quiz 3 (45 min) Quiz 3 solutions 
Reading: R. Schapire, A brief introduction to boosting 
Tuesday Feb. 21 
Reading: Chapter 8 from Introduction to Data Mining by Tan, Steinbach, and Kumar, pp. 487515, 532540, 546550. 

Thursday Feb. 23 
Unsupervised learning, continued 
Homework 5 (Kmeans Clustering), due Tuesday March 7: Assignment 
Tuesday Feb. 28 
Quiz 4 (30 min) Quiz 4 solutions Unsupervised learning, continued 
Reading: pp. 278280 of Reinforcement Learning for True Adaptive Traffic Signal Control (a brief and clear introduction to Qlearning) 
Thursday March 2 
Reinforcement learning, continued 

Tuesday March 7 
Homework 6 (Reinforcement Learning), due Thursday March 16 : Assignment 

Thursday March 9 
Quiz 5 (30 min) Quiz 5 solutions Deep learning, continued 

Tuesday March 14 
Guest lectures: Will Landecker: Data Science in the Real World (pdf) Sheng Lundquist:Using Tensorflow for Deep Learning (pdf) 

Thursday March 16 
Review for final exam 

Monday March 20 
Final exam: 10:15am  12:05pm 
