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course_outline

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

course_outline.txt · Last modified: 2016/09/12 21:11 by hj

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