Topics in Artificial Intelligence -- Machine Learning with Large-scale Data (original) (raw)

General Information

Overview

This graduate-level course covers machine-learning algorithms, programming environments, and software frameworks that are designed to effectively deal with large-scale (i.e., big) data.

Prerequisites: A previous course on machine learning or data mining. A strong knowledge of algorithms and programming (Java, C, and scripting/dynamic languages).

Textbook

• Scaling up Machine Learning: Parallel and Distributed Approaches. Edited by Ron Bekkerman, Mikhail Bilenko, and John Langford. Cambridge University Press, December 30, 2011

Resources

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

You will be evaluated based on student presentations (40%) and a substantial semester-long project (60%). The project must include at least one big data set, at least one learning/mining algorithm, and a real-world application. For the project, you will need to prepare a proposal, give a presentation at the end of the semester, and write a final report. More details will be provided in class.

Notes, Policies, and Guidelines

• We will use the class sakai site for announcements, assignments, and your contributions.
• When emailing me about the course, begin the subject line with [f14 cs598].
• For your hadoop-based jobs, you can use the DCS hadoop cluster. For big non-hadoop jobs, you can use aurora.cs. If you don't have accounts on these machine, let me know.
• Course projects must be done individually.
• 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)

Some Similar Courses in Other Universities

• Machine Learning with Large Datasets by William Cohen (taught Spring 2012, Spring 2013 and Spring 2014)
• Big Data: Large Scale Machine Learning by John Langford and Yann LeCun (taught Spring 2013)
• Machine Learning for Big Data / Statistics for Big Data by Carlos Guestrin and Emily Fox (taught Winter 2013)