The following is a tentative list of topics that will be covered in the course, application topics for assignments, and related reading materials. (Subject to change.)
Techniques/topics:
Supervised
Regression
Linear regression
Logistic regression
Classification
Naive bayes
K-nearest neighbors
Neural networks
Support vector machines
Boosting
Kernel methods
Unsupervised
K-means
Mixture models
Spectral methods
Principal components analysis
Factor analysis
Non-negative matrix factorization
Probabilistic inference
Graphical models
Approximate inference
Sampling
Gibbs
MCMC
Variational methods
Model assessment
Cross-validation
ROC curve
Bayesian occam’s razor
Information-theoretic measures
Data representation
Feature spaces
Feature selection
Similarity measures
Practical applications:
Spam filtering
Recommendation systems
Topic discovery in documents
Search engine ranking
Reading materials:
(note: a subset of the listed chapters will be selected)
“Collective Intelligence”, Toby Segaran [2007] (Chapters 3,5,6,9)
“The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Trevor Hastie, Robert Tibshirani, & Jerome Friedman [2001] (Chapters 1,3,4,6,12,14)
“Pattern Recognition and Machine Learning”, Christopher Bishop [2006] (Chapters 3,4,7,8,9,11,12)