COS 324 - Introduction to Machine Learning

Fall 2017

News: Jan  05  Final study guide  is availbable. Oct  25  Midterm cheat sheet  is availbable. Oct  23  Midterm study guide  is availbable. Sept 19  Assignment 1 through 5 have been released.

Course Description

The course provides an introduction to machine learning.

Topic covered:

• Online learning and decision making
• Learning from examples and generalization
• Empirical risk minimization and regularization
• Introduction to convex analysis
• Gradient-based learning
• Implementation and analysis of learning algorithms for regression, binary classification, multiclass categorization, and ranking problems
• Dimensionality reduction methods
• Ensemble methods and boosting
• Neural networks and deep learning
• Markov decision precesses

Grading:

• Homework assignments: 40%
• Midterm: 20%
• Final exam: 40%
• Bonus questions in class
• Attendance is mandatory

Midterm: The midterm will be during class time on Thu, Oct 26. No exceptions or alternate times will be offered.

Please join our Piazza.

Textbook and readings

NOTICE: All material of the course is self-contained and based on freely available books and surveys.

Main references:

• Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David
• Online convex optimization, by Elad Hazan
• Machine Learning, by Tom Mitchell
• An Introduction to Computational Learning Theory, by Michael Kearns & Umesh Vazirani
• Machine Learning: A Probabilistic Perspective, by Kevin Murphy,

Further advanced references:

• Convex Optimization, by Stephen Boyd and Lieven Vandenberghe
• Convex optimization: algorithms and complexity, by Sebastien Bubeck
• Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig

Python Tutorials

• An interactive python tutorial from LearnPython.com
• Tutorial for Python 2.7 from python.org
• Tutorial for Python 3 from python.org

Lectures

Tuesday and Thursday 11:00-12:20, in Computer Science Building Rm 104

Precepts

• P01 (Nikunj): Thu 7:30 - 8:20pm, Friend 110
• P01A (Cyril): Thu 7:30 - 8:20pm, Sherrerd 001
• P02 (Wei): Fri 1:30 - 2:20pm, Friend 112
• P03 (Yi): Fri 2:30 - 3:20pm, Friend 112
• P03A (Karan): Fri 2:30 - 3:20pm, Friend 009

Staff

Instructor: Prof. Elad Hazan and Prof. Yoram Singer

TAs: Wei Hu, Nikunj Saunshi, Karan Singh, Cyril Zhang, Yi Zhang

Undergrad coordinator: Colleen Kenny-McGinley (ckenny at cs, CS 210)

Questions

We will be using piazza for Q&A. Please post your questions there instead of mailing the Professor or TAs.

Professors' office hours:

• Prof. Hazan: Mon 9-10am in CS 409
• Prof. Singer: Thu 4-5pm in CS 421

TA office hours:

• Karan: Fri 1:30-2:30pm in CS 431
• Cyril: Thu 6:15-7:15pm in CS 431
• Nikunj: Thu 6:15-7:15pm in CS 431
• Wei: Tue 3:30-4:30pm in CS 431
• Yi: Mon 3:30-4:30pm in CS 431