Lionsolver Learning From Data Homework

[Fall 2014] Topics in Artificial Intelligence: Machine Learning with Large-scale Data

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

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

Schedule / Syllabus (Subject to Change)

Some Similar Courses in Other Universities


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