Overview

Machine learning is an exciting and fast-moving field of computer science with many recent consumer applications (e.g., Microsoft Kinect, Google Translate, Iphone's Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine (e.g., predicting protein-protein interactions, species modeling, detecting tumors, personalized medicine). In this undergraduate-level class, students will learn about the theoretical foundations of machine learning and how to apply machine learning to solve new problems.

General information

Lectures: Tuesday and Thursday, 2pm-3:15pm
Room: Warren Weaver Hall 312

Instructor:		Prof. David Sontag		dsontag {@ | at} cs.nyu.edu
TA:		Kevin Jiao		jj1745 {@ | at} nyu.edu
Graders:		Alexandre Sablayrolles		sla382 {@ | at} nyu.edu
		Yijun Xiao		ryjxiao {@ | at} nyu.edu

Office hours (David): Tuesdays 4:30-5:30pm. Location: 715 Broadway, 12th floor, Room 1204
Office hours (Kevin): Wednesdays 6:30-8:00pm. Location: KMC 8-150

Grading: problem sets (50%) + midterm exam (25%) + project (20%) + participation (5%). Problem Set policy

Pre-requisites: Students must either have taken Basic Algorithms (CSCI-UA.0310) or be taking it concurrently. Linear algebra (MATH-UA 140) is strongly recommended as a pre-requisite, and knowledge of multivariable calculus will be helpful. Students should also have good programming skills.

Books: No textbook is required (readings will come from freely available online material). If an additional reference is desired, the following books are good options. Bishop's book is easier to read, whereas Murphy's book has more depth and coverage (and is up to date).

• Machine Learning: a Probabilistic Perspective, by Kevin Murphy (2012).
• Pattern Recognition and Machine Learning, by Chris Bishop (2006).

Piazza: We will use Piazza to answer questions and post announcements about the course. Please sign up here. Students' use of Piazza, particularly for adequately answering other students' questions, will contribute toward their participation grade.

Project information

Reference materials

• Machine learning books
  
• Trevor Hastie, Rob Tibshirani, and Jerry Friedman, Elements of Statistical Learning, Second Edition, Springer, 2009. (Can be downloaded as PDF file.)
• David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012. (Can be downloaded as PDF file.)
• Shai Shalev-Shwartz, and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014. (Can be downloaded as PDF file.)
  
• Probability
• Chapter 2 of either Murphy or Bishop (see also Bishop Appendix B)
• Review notes from Stanford's machine learning class
• Sam Roweis's probability review
  
• Linear algebra
• Bishop Appendix C
• Online class from MIT
• Review notes from Stanford's machine learning class
• Sam Roweis's linear algebra review
  
• Calculus
• Bishop Appendix D and E (Lagrange multipliers)
• Notes from MIT on Lagrange multipliers
• Dan Klein's Lagrange Multipliers without Permanent Scarring
• Optimization
• Convex Optimization by Stephen Boyd and Lieven Vandenberghe. (Can be downloaded as PDF file.)
  

1. You may discuss a problem with any student in this class, and work together on solving it. This can involve brainstorming and verbally discussing the problem, going together through possible solutions, but should not involve one student telling another a complete solution.

2. Once you solve the homework, you must write up your solutions on your own, without looking at other people's write-ups or giving your write-up to others.

3. In your solution for each problem, you must write down the names of any person with whom you discussed it. This will not affect your grade.

4. Do not consult solution manuals or other people's solutions from similar courses.