CS 534  Machine Learning
Course Overview
Machine learning impacts many applications including the sciences (e.g., predicting genomeprotein interactions, detecting tumors, personalized medicine) and consumer products (e.g., Amazon’s Alexa, Microsoft Kinect, Neflix). In this course, students will learn the fundamental theory and algorithms of machine learning. Students will also obtain practical experience applying standard machine learning methods to solve a variety of problems.
Prerequisites:
 Undergraduatelevel linear algebra
 Undergraduatelevel probability
 Undergraduatelevel algorithms
 Exposure to statistics
 Programming ability in Python, Matlab, Julia, R, or C++
Course Logistics
 Piazza: All annoucements, assignment clarifications, and slide corrections will be posted here. Make sure you check it on a regular basis.
 Textbook
 Required: The Elements of Statistical Learning: Data Mining, Inference, and Prediction), by Trevor Hastie, Robert Tibshirani & Jerome Friedman
 Supplemental: Machine Learning: a Probabilistic Perspective, by Kevin Murphy
 Supplemental: Pattern Recognition and Machine Learning, by Christopher Bishop
 Supplemental: Understanding Machine Learning:From Theory to Algorithms, by Shai ShalevShwartz & Shai BenDavid
 Supplemental: A Course in Machine Learning, by Hal Daumé III
 Office Hours
 Joyce Ho: Tu 2:303:30 PM; Wed 9:0010:00 AM; By Appointment (MSC W414)
 Huan He: Fri 9:0011:00 AM (MSC E308)
(Tentative) Course Schedule
The reading material listed below is optional and the lecture plan may deviate over the course of the semester.
#  Date  Topic  Reference (Chapter)  Assignment 

1  8/30  Intro + Course Logistics  Ch. 1 (Hastie et al.) Ch. 1 (Murphy) 
Homework #0 (Due 9/2) 
2  9/4  Intro to Optimization  Convex optimization notes Part I and II from Stanford’s machine learning class Rosenberg’s abridged notes 

3  9/6  Intro to Statistics and Regression  
4  9/11  Regression  Ch 3.1  3.4 (Hastie et al.) Ch. 17.1  17.2 (Barber) Prof. Carlos Carvalho’s MLR Slides 

5  9/13  Regression + Naive Bayes  Homework #1 (Due 9/28)  
6  9/18  Linear Classification  
7  9/20  Linear Classification + BiasVariance Tradeoff  
8  9/25  Model Assessment  
9  9/27  Model Selection  Homework #2 (Due 10/12)  
10  10/2  Model Selection + Bootstrap  
11  10/4  Trees + Additive Models  
12  10/11  Boosting  Homework #3 (Due 10/26)  
13  10/16  Random Forest  Ch. 15, 16 (Hastie et al.) Breiman’s paper on random forests 

14  10/18  Random Forest & Ensembles  
15  10/23  Support Vector Machines  Ch. 12 (Hastie et al.) Ch. 15 (ShalevShwartz & BenDavid) SVM notes from Stanford’s ML class SVM notes from NYU’s ML class Ch. 11 (Daume) 

16  10/25  Support Vector Machines  Homework #4 (Due 11/14)  
17  10/30  Dimensionality Reduction  Ch. 14 (Hastie et al.) PCA notes from Stanford’s ML class Ch. 23 (ShalevShwartz & BenDavid) 

18  11/1  Dimensionality Reduction  
19  11/6  Midterm Exam  
20  11/8  Project Madness + Things to Know  
21  11/13  KNearest Neighbors  Ch. 13 (Hastie et al.) Ch. 3.2  3.3 (Daume) Ch. 19.1  19.2 (ShalevShwartz & BenDavid) 
Homework #5 (Due 11/28) 
22  11/15  Neural Networks  Ch. 11 (Hastie et al.) Ch. 13 (Nielsen) Ch. 20.1  20.3 (ShalevShwartz & BenDavid) 

23  11/20  Neural Networks  
24  11/27  Crash Course in Deep Learning  
25  11/29  Applications (Recommendation Systems)  
26  12/4  Presentations  
27  12/6  Presentations  
28  12/11  Presentations 
Course Grading
Component  Weight 

Homeworks  35% 
Midterm  15% 
Project  40% 
Participation  10% 
Project
You are encouraged to work in groups of 23 for the final project. The goal is to either develop a novel algorithm (novelty bonus points will be given depending on the level of difficulty) or try various ML existing algorithms on the dataset. The project is a critical part of the course and a significant factor in determining your grade. Teams are required to hand in a project proposal, a final project report and prepare two presentations on their work.
By default, all team members will receive the same score for their project. If a team feels that this is unfair perhaps due to HIGHLY imbalanced contributions, then every team member needs to provide feedback on the contribution of each of the other team members via email before submission of the final report. After that I will have a meeting with the entire group to mediate.
More details on projects are posted on Piazza under the projects folder.
Component  Due Date  Weight 

Proposal  10/25  15% 
Madness  11/8  10% 
Presentation  12/412/11  25% 
Report  12/12  50% 
Assignment and Exam Policy
 Assignments
 Assignments (homeworks 15, final projects) are due electronically on Canvas at 11:59 PM.
 Each student receives 6 late days that can be used across the 5 homeworks throughout the semester. These late days extend the deadline for 24 hours.
 A maximum of 3 late days can be used on a given homework.
 Late days apply to the entire homework, so handing in one problem late counts as a late day towards the whole homework.
 No credit will be given if you submit the homework late and have no remaining late days.
 Exam
 The midterm (openbook, opennotes, no electronic devices) must be taken at the required time.
 Requests for rescheduling the midterm exam will only be considered if the request is made at least a week prior to the exam date.
Honor Code
All class work is governed by the College Honor Code and Departmental Policy. It is acceptable and encouraged to discuss homeworks with other students. However, this should be noted on your submitted homework and all code and writeup must be written by yourself. Any code and writeup that is found to be similar is grounds for an honor code investigation by the Director of Gradute Studies, Laney Graduate School, and the honor council. Additional extensions on assignments will be granted with appropriate documentation from the Office of Undergraduate Education (OUE)