Tentative Syllabus:

Supervised learning (learn to predict):

• linear regression

• linear classifiers

• decision trees

• ensemble methods
  

• evaluation of supervised learning

• agglomerative clustering

• k-means clustering

• dimensionality reduction and visualization

• frequent pattern mining using the Apriori algorithm

• Markov decision processes    

• Q-learning

Course materials:

• No text book required, lecture notes and reading materials will be posted on the webpage, please check regularly.

• Here are some useful books for references.
  
• Machine learning, Tom  Mitchell, McGraw-Hill (Referred to as TM)
  
• Machine learning and pattern recognition, Chris Bishop, Springer (referred to as Bishop)

Prerequisite: CS325