Time: Tuesdays & Thursdays 3:20-4:40 PM | Place: CBIM 22 [NEW LOCATION] |

Instructor: Tina Eliassi-Rad | Office hours: Thursdays 5:00-6:00 PM in CBIM 08 |

TA: Chetan Tonde | TA office hours: Wednesdays 3:00-4:00 PM in CBIM (cubicle near printer room) |

Also, available by appointment. Email cjtonde [at] cs [dot] rutgers [dot] edu to setup appointment. | |

Course number: 16:198:536 | Credits: 3 |

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

- (
*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.

- 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)

- 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:
- What is the problem?
- Why is it interesting and important?
- Why is it hard? Why have previous approaches failed?
- What are the key components of your approach?
- 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

- 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!