Automatic learning

Machine learning fundamentals: supervised/unsupervised learning, classification/regression, optimization, overlearning, generalization, etc.
Linear regression and probabilistic modeling (maximum likelihood, maximum a posteriori)
Linear classifiers: logistic regression, gradient descent, Bayesian classifier
SVM and kernel methods
Decision trees and model combination (bagging, boosting, etc.).
Unsupervised learning (clustering) and dimension reduction
Time series processing, Markov chains
Introduction to natural language processing (NLP)