Schedule
Spring 2011, fourth quarter, weeks 1421  Monday 16:0018:00 in IMADA Seminarrum 
First lecture: April 4, 2011.  Wednesday 12:1514:00 in IMADA Seminarrum 
Last lecture: May 27, 2011.  Friday 14:0016:00 in IMADA Seminarrum 
Lectures
Lec.  Date  Topic  Literature and Assignments 
L0  04.11.2010  Presentation  
L1  04.04.2011  introduction, linear regression, knearest neighbor  [B2 12.4; B3, 3.1.3; B6 5.15.10] [ exercises S1 ] 
L2  08.04.2011  model selection, linear models, probability interpretation  [B1 sc1.11.4, sc3.1; B2 sc7.17.3, sc7.107.11] [ exercises S2 ] 
L3  11.04.2011  Bayesian approach, logistic regression, generalized linear models  [B1 sc1.5 2.1, 2.2, 2.4, 3.1, 3.3.1, 3.3.2, 4.2, 4.3] 
E1  13.04.2011   [ exercises S3 ] [ solutions ] 
L4  15.04.2011  neural networks, multilayer perceptron  [B1 sc5.1, 5.2, 5.3] [ exercises S4 ] [ solutions ] 
L5  18.04.2011  neural networks, generative algorithms, GDA, naive Bayes  [B1 sc5.5, 4.2; B2 ch11; L1; L2] 
L6  27.04.2011  linear methods for classification  [B2 ch4; B1 sc7.1] [ obligatory assignment 1 ] [ exercises S5 ] 
L7  02.05.2011  support vector machines and kernels  [B2 sc2.8.2, ch6, sc12.112.3.5; B1 sc2.5, sc77.1.5; A3 sc119131; A1; L3] 
L8  04.05.2011  learning theory  [B1 sc7.1.5] [ exercises S6 ] 
L9  06.05.2011  probabilistic graphical models  [B1 ch8] 
L10  09.05.2011  probabilistic graphical models  [B1 ch8] [ exercises S7 ] [ solutions ] 
L11  13.05.2011  probabilistic graphical models, inference  [B1 ch8] [B4 sc14.5(e)] [ exercises S8 ] [ solutions ] 
L12  16.05.2011  mixtures models, EM algorithm, hidden Markov models  [B1 ch9; B1 sc12.1; B1 sc13.113.2; B2 ch14.3,14.5] [ exercises S9 ] [ solutions ] 
L13  18.05.2011  bagging, boosting, tree based methods  [B1 sc1.6, 14.114.4; B2 sc9.2] [ obligatory assignment 2 ] [ exercises S10 ] 
Literature
Other references

[B3] S. Marsland. Machine Learning: An Algorithmic Perspective. CRC Press,
Taylor and Francis group, 2009

[B4] S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 2010.

[B5] D. Koller and N. Friedman. Probabilistic Graphical Models. Principles and Techniques. MIT Press, 2009, 399

[B6] M.H. Kutner, C.J. Nachtsheim, J. Neter and W. Li. Applied Linear
Statistical Models. McGrawHill, 2005

[B7] W.N. Venables and B.D. Ripley. Modern Applied Statistics with S. Springer, Fourth Edition. 2002.

[B8] F.V. Jensen and T.D. Nielsen. Bayesian Networks and Decision Graphs. Springer New York, 2007.
Evaluation

Mandatory assignments, pass/fail, internal evaluation by the teacher.
The mandatory assignments include programming work. The assignments
must be passed before the written exam can be attended.

Written exam on June 22, 9.0012.00, U49

Reexam: January 5, 2012

Syllabus
Author: Marco Chiarandini
<marco@imada.sdu.dk>
Date: 20110729 11:18:36 CEST
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