Machine Learning (original) (raw)

Spring 2014: Machine Learning

General Information

Overview

This graduate-level course introduces the theory, algorithms, and applications of machine learning. Topics covered include supervised learning, unsupervised learning, semi-supervised learning, learning theory, and reinforcement learning.

Prerequisites: Calculus and linear algebra. An introductory course on statistics and probability. Algorithms and programming (MATLAB).

Textbooks

• (Required) Kevin Murphy,Machine Learning: A Probabilistic Perspective. ISBN 0262018020, MIT Press, 2012.
• (Required) Christopher Bishop, Pattern Recognition and Machine Learning. ISBN 0387310738, Springer 2006.
• (Recommended) Tom Mitchell, Machine Learning. ISBN 0070428077, McGraw-Hill, 1997.
• (Recommended; free on-line) Trevor Hastie, Robert Tibshirani and Jerome Friedman, Elements of Statistical Learning. ISBN 0387952845, Springer, 2009 (2nd edition).
• (Recommended; free on-line) David MacKay, Information Theory, Inference, and Learning Algorithms. ISBN 0521642981, Cambridge University Press, 2003.
• (Optional; free on-line) Roberto Battiti and Mauro Brunato.The LION Way: Machine Learning plus Intelligent Optimization. Lionsolver, Inc. 2013.

Resources

• Mathworks Matlab Tutorials
• Ben Taskar's Matlab Tutorial
• Probability Review (David Blei, Princeton)
• Probability Theory Review (Arian Maleki and Tom Do, Stanford)
• Linear Algebra Tutorial (C.T. Abdallah, Penn)
• Linear Algebra Review and Reference (Zico Kolter and Chuong Do, Stanford)
• Statistical Data Mining Tutorials (Andrew Moore, Google/CMU)
• Theoretical CS Cheat Sheet (Princeton)

Grading

• Homework assignments (3×10%)
• Midterm (15%)
• Final exam (25%)
• Class project (30%)
  • Proposal report (10%) -- 2 pages plus 5-minute pitch Should include answers to the following questions:
    1. What is the problem?
    2. Why is it interesting and important?
    3. Why is it hard? Why have previous approaches failed?
    4. What are the key components of your approach?
    5. What data sets and metrics will be used to validate the approach?
  • In-class presentation (10%)
  • Final report (10%) -- 6 pages max
    • For guidance on writing the final report, see slide 70 of Eamonn Keogh's KDD'09 Tutorial on How to do good research, get it published in SIGKDD and get it cited!
    • Follow ACM formatting guidelines

Notes, Policies, and Guidelines

• We will use the class sakai site for announcements, assignments, and your contributions.
• When emailing me or the TA about the course, begin the subject line with [sp14 cs536].
• Programming exercises will be in MATLAB. Rutgers holds a site license for MATLAB. You can download MATLAB to your computer from the university's software portal. MATLAB is also installed on the CS machines. Just type "matlab" at the prompt.
• Homeworks must be done individually. Late homeworks are accepted up to 4 days after the deadline. A penalty of 20% be charged for each late day.
• The class project can be done either individually or in groups of two.
• For your class project, you can use whatever programming language that you like.
• Any regrading request must be submitted in writing and within one week of the returned material. The request must detail precisely and concisely the grading error.
• Refresh your knowledge of the university's academic integrity policy and plagiarism. There is zero-tolerance for cheating!

Schedule / Syllabus (Subject to Change)

Other Topics of Interest

• Anomaly Detection
• Large-Scale Machine Learning
• Probabilistic Topic Models
• Transfer Learning