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- Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
- Ethem Alpaydın, Introduction to Machine Learning. Second Edition.
- Larry Wasserman, All of Statistics. Springer, 2010.
- Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction. MIT Press, 1998.
- Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning. Springer, 2009.
- WEKA Machine learning software in JAVA that you can use for your projects
- Data Mining with Weka A MOOC Course
- ABAGAIL Machine learning software in JAVA. This is hosted on my github, so you can contribute too
- scikit-learn A popular python library for supervised and unsupervised learning algorithms
- 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.
- Murphy's MDP Toolbox for Matlab
- MATLAB Clustering Package By Frank Dellaert
- ICA Example
- UCI Machine Learning Repository An online repository of data sets that can be used for machine learning experiments.
- Stanford Large Network Dataset Dataset of large social and information networks.
- Vision Benchmark Suite Autonomous car dataset
- Other datasets
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).
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