Maching Learning

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Course Resources

Reading Materials

Suggested Text

  1. Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
  2. Ethem Alpaydın, Introduction to Machine Learning. Second Edition.

Optional Text

  1. Larry Wasserman, All of Statistics. Springer, 2010.
  2. Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction. MIT Press, 1998.
  3. Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning. Springer, 2009.

Reading List

Coding Resources


  1. WEKA Machine learning software in JAVA that you can use for your projects
  2. Data Mining with Weka A MOOC Course
  3. ABAGAIL Machine learning software in JAVA. This is hosted on my github, so you can contribute too
  4. scikit-learn A popular python library for supervised and unsupervised learning algorithms
  5. MATLAB NN Toolbox The toolbox supports supervised learning with feedforward, radial basis, and dynamic networks and unsupervised learning with self-organizing maps and competitive layers.
  6. Murphy's MDP Toolbox for Matlab
  7. MATLAB Clustering Package By Frank Dellaert
  8. ICA Example


  1. UCI Machine Learning Repository An online repository of data sets that can be used for machine learning experiments.
  2. Stanford Large Network Dataset Dataset of large social and information networks.
  3. Vision Benchmark Suite Autonomous car dataset
  4. Other datasets

Downloadable Materials

You can download Supplemental Materials, Lesson Videos and Transcripts from Downloadables (bottom right corner of the Classroom) or from the Dashboard (first option on the navigation bar on the left hand side).

Course Syllabus

Lesson 1: Decision Trees

Lesson 1 Slides

Lesson 2: Regression & Classification

Lesson 2 Slides

Lesson 3: Neural Networks

Lesson 3 Slides

Lesson 4: Instance Based Learning

Lesson 4 Slides

Problem Set 1

Lesson 5: Ensemble B&B

Lesson 5 Slides

Lesson 6: Kernel Methods & SVMs

Lesson 6 Slides

Lesson 7: Comp Learning Theory

Lesson 7 Slides

Lesson 8: VC Dimensions

Lesson 8 Slides

Lesson 9: Bayesian Learning

Lesson 9 Slides

Lesson 10: Bayesian Inference

Lesson 10 Slides

Problem Set 2