====== Course Outline ======

The course outline is a guideline to topics that will be discussed in the course, and when they will be discussed:

===== Weeks 1 and 2 =====

**Machine Learning** (basic concepts)

**Math Background**: probabilities; Bayes theorem; statistics; estimation; regression; hypothesis testing; entropy; mutual information; decision tree; optimization theory and convex optimization; matrix decomposition 

===== Week 3 =====

**Data, Feature and Model**:  Feature Engineering, Feature Extraction (PCA, LDA, etc), Data Virtualization  

===== Week 4 =====

**Machine Learning (ML) Basics**:  Learnability; Some basic ML Concepts; Bayesian decision theory

===== Week 5 =====

**Generative Models (I)**: model estimation: maximum likelihood, Bayesian learning, multivariate Gaussian, Multinomial, Markov Chain model, etc.

===== Week 6 =====

**Discriminative Models (I) **: Linear discriminant functions; support vector machine (SVM); large margin classifiers; sparse kernel machines;


===== Week 7 =====

**Discriminative Models (II) **:  Neural Networks and Deep Learning

===== Week 8  =====

**Generative Models (II)**: EM algorithm; Finite Mixture models, e.g. Gaussian mixture model

===== Week 9  =====

**Generative Models (III)**:  graphical models, directed vs. indirected graphical models, exact inference, approximate inference (loopy belief propagation, variational inference, Monte Carlo methods)

===== Week 10  =====

**Selected Advanced Topics**: Hidden Markov Model (HMM), HMM concepts; Three algorithms: forward-backward; Viterbi decoding; Baum-Welch learning. 
 
===== Weeks 11-12 =====

student presentation