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Fall 2015: Pattern Recognition: Theory & Applications

General Information

Time: Mondays & Thursdays 12:00-1:20 PM	Place: LIV RC-3
Instructor: Tina Eliassi-Rad	Office hours: Mondays 2:00-3:00 PM in CBIM 08
TA: Chetan Tonde	TA office hours: Mondays 3:00-5:00 PM in CBIM 22
	Also, available by appointment. Email cjtonde [at] cs [dot] rutgers [dot] edu to setup appointment.
Course number: 16:198:535	Credits: 3

Overview

This graduate-level course covers unsupervised learning.

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

Textbooks

This course does not have a designated textbook. The readings are assigned in the syllabus. Here are some textbooks (all optional) related to the course.

• Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. ISBN 0387952845. (free online)
• Anand Rajaraman, Jurij Leskovec, and Jeffrey Ullman. Mining of Massive Datasets. v2.1, Cambridge University Press. 2014. (free online)
• Christopher Bishop. Pattern Recognition and Machine Learning. ISBN 0387310738.
• Tom Mitchell. Machine Learning. ISBN 0070428077.
• Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. ISBN 0262018020.
• Peter Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. ISBN 1107422221.
• David J. Hand, Heikki Mannila, Padhraic Smyth. Principles of Data Mining. ISBN 026208290X.
• Jiawei Han, Micheline Kamber, Jian Pei. Data Mining: Concepts and Techniques, Third Edition. ISBN 0123814790.
• Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. ISBN 0321321367.

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 (2×15%)
• Midterm exam (30%)
• Class project (40%)
• 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 (12%)
• Final report (18%) -- 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

Schedule / Syllabus (Subject to Change)

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 [f15 cs535].

• 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 2 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!
• Letter grades will be assigned based on Rutgers Graduate Grade Scale, which is as follows:
  

  A in [90, 100]

  B+ in [85, 89.99]

  B in [80, 84.99]

  C+ in [75, 79.99]

  C in [70, 74.99]

  F in [0, 69.99]