What are ML algorithms, how did they come about, where are they today, what to expect in 5-10 years. Computational foundations, societal impact. Refresher (test?) of basics of descriptive statistics, linear algebra, optimization, information theory.
 Data collection, standard data sets, repositories. Survey of major application domains: speech- and character recognition (ASR, OCR), (biometric) identification, pattern classification, ranking/recommendation, info extraction, info retrieval, natural language processing (NLP).
 Principal component analysis, linear discriminant analysis, max margin classifiers, data reduction, feature engineering.
 Maximum entropy methods, decision trees.
 Genetic/evolutionary methods, boosting
 Midterm exam
 Nearest neighbor, tangent distance methods.
 Algorithmic information theory, Kolmogorov complexity, minimum description length.
 Hidden Markov Models (HMM), Viterbi, EM.
 Learning with multiple goals.
 Neural nets (NN), backpropagation.
 Final exam