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