Reading List

• Topics, Chapter and Video Reference and Dates. Google Docs Link

Tutorials

• Think Stats, Exploratory Data Analysis in Python

• Linear Regression Using Weka

Software

• Weka: http://www.cs.waikato.ac.nz/ml/weka/

• MATLAB or Octave: Coursera Machine Learning suggests to use this.

• scikit: Udacity Introduction to Machine Learning suggests on using this.

• Weka Tutorial

• StackOverflow question for XOR case

• Weka Command Line Options

• Weka For Regression

Books

• Mitchell’s Book.

Courses

• Introduction to Machine Learning in Udacity

  • Introduction to Inferential Statistics

  • Introduction to Descriptive Statistics

• Introduction to Machine Learning in Coursera

  • Familiarity with the basic probability theory. (CS109 or Stat116 is sufficient but not necessary.)

  • Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)

Presentation

• Andrew Moore’s tutorial

• Bottom up Pruning

• Computational Learning Theory

[1]: https://www.cis.upenn.edu/~mkearns/papers/pruning.pdf

References

• Michael Jordan

Chapter References

• Bayesian Learning Extension

Lectures Duration

• Lecture Duration (min)

• ML IS THE ROX 22

• SL 1 - Decision Trees 104

• SL 2 - Regression & Classification 48

• SL 3 - Neural Networks 61

• SL 4 - Instance Based Learning 75

• SL 5 - Ensemble B&B 79

• SL 6 - Kernel Methods & SVMs 84

• SL 7 - Comp Learning Theory 91

• SL 8 - VC Dimensions 44

• SL 9 - Bayesian Learning 89

• SL 10 - Bayesian Inference 74

• UL 1 - Randomized Optimization 146

• UL 2 - Clustering 78

• UL 3 - Feature Selection 51

• UL 4 - Feature Transformation 84

• UL 5 - Info Theory 21

• RL 1 - Markov Decision Processes 122

• RL 2 - Reinforcement Learning 57

• RL 3 - Game Theory 112

• RL 4 - Game Theory Continued 101

• Outro 27

• Lecture Schedule