Differences between revisions 2 and 15 (spanning 13 versions)

Lecture “Bayesian Learning”

General Information

Lecture	Thursdays 14-16 (22.10, 29.10, 5.11.2015, 7.1, 14.1, 21.1.2016)
Room	MAR 4.062
Teachers	Shinichi Nakajima
Contact	nakajima@tu-berlin.de
Language	English
Credits	3 ECTS, Elective Course in Machine Learning Module I (computer science M.Sc.)

Topics

Bayesian learning is a category of machine learning methods, which are based on a basic law of probability, called Bayes’ theorem. As advantages, Bayesian learning offers assessment of the estimation quality and model selection functionality in a single framework, while as disadvantages, it requires “integral” computation, which often is a bottleneck. In this course, we introduce Bayesian learning, discuss pros and cons, how to perform the integral computation based on “conjugacy”, and how to approximate Bayesian learning when it is intractable.

The course covers

Bayesian modeling and model selection.
Bayesian learning in conjugate cases.
Approximate Bayesian learning in conditionally conjugate cases:
- Gibbs sampling
- variational Bayesian learning.
Approximate Bayesian learning in non-conjugate cases:
- Metropolis-Hastings algorithm.
- local variational approximation.
- expectation propagation.

The lecture will be given on a blackboard except the introductionBayesianLearningIntroduction.pdf

Home works

The students who need credits must submit an answer sheet to the two homeworks by the due dates.

Homework 1: BayesianLearning_HW1.pdf data.txt (due data: 7.1.2016)
Homework 2: will be available after the lecture on 21.1.2016 (due data: 21.2.2016)

IDA Wiki: Main/WS15_BayesianLearning (last edited 2016-01-26 02:16:56 by ShinichiNakajima)

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-|| Lecture || Thursdays 14-16 ||
+|| Lecture || Thursdays 14-16 ('''22.10, 29.10, 5.11.2015, 7.1, 14.1, 21.1.2016''') ||
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+|| Language || English ||
|| Credits || 3 ECTS, Elective Course in Machine Learning Module I (computer science M.Sc.)||
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-* Bayesian modeling and model selection.
* Bayesian learning in conjugate cases.
* Approximate Bayesian learning in conditionally conjugate cases, e.g., 
   * Gibbs sampling and variational Bayesian learning.
* Approximate Bayesian learning in non-conjugate cases, e.g.,
   * Metropolis-Hastings algorithm, local variational approximation and expectation propagation.
+ * Bayesian modeling and model selection.
 * Bayesian learning in conjugate cases.
 * Approximate Bayesian learning in conditionally conjugate cases:
  * Gibbs sampling
  * variational Bayesian learning.
 * Approximate Bayesian learning in non-conjugate cases:
  * Metropolis-Hastings algorithm.
  * local variational approximation.
  * expectation propagation.

The lecture will be given on a blackboard except the introduction[[attachment:BayesianLearningIntroduction.pdf]] 

== Home works ==

The students who need credits must submit an answer sheet to the two homeworks by the due dates.

 * Homework 1: [[attachment:BayesianLearning_HW1.pdf]]  [[attachment:data.txt]] (due data: '''7.1.2016''')
 * Homework 2: will be available after the lecture on 21.1.2016 (due data: '''21.2.2016''')