Differences

This shows you the differences between two versions of the page.

--- course_outline [2007/07/31 19:53] – external edit 127.0.0.1
+++ course_outline [2016/09/12 21:11] (current) – hj
@@ Line 3: / Line 3: @@
 The course outline is a guideline to topics that will be discussed in the course, and when they will be discussed:
-===== Week 1 =====
+===== Weeks 1 and 2 =====
-Your notes here.
+**Machine Learning** (basic concepts)
-===== Week 2 =====
+**Math Background**: probabilities; Bayes theorem; statistics; estimation; regression; hypothesis testing; entropy; mutual information; decision tree; optimization theory and convex optimization; matrix decomposition
-===== Midterm =====
+===== Week 3 =====
-===== Drop Deadline =====
+**Data, Feature and Model**:  Feature Engineering, Feature Extraction (PCA, LDA, etc), Data Virtualization
-===== Week 13 =====
+===== 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
-===== Final Exam =====