DM825 - Introduction to Machine Learning

Schedule

Spring 2011, fourth quarter, weeks 14-21	Monday 16:00-18:00 in IMADA Seminarrum
First lecture: April 4, 2011.	Wednesday 12:15-14:00 in IMADA Seminarrum
Last lecture: May 27, 2011.	Friday 14:00-16:00 in IMADA Seminarrum

Lec.	Date	Topic	Literature and Assignments
L0	04.11.2010	Presentation
L1	04.04.2011	introduction, linear regression, k-nearest neighbor	[B2 1-2.4; B3, 3.1.3; B6 5.1-5.10] [ exercises S1 ]
L2	08.04.2011	model selection, linear models, probability interpretation	[B1 sc1.1-1.4, sc3.1; B2 sc7.1-7.3, sc7.10-7.11] [ 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, multi-layer 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, sc12.1-12.3.5; B1 sc2.5, sc7-7.1.5; A3 sc119-131; 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 sc13.1-13.2; B2 ch14.3,14.5] [ exercises S9 ] [ solutions ]
L13	18.05.2011	bagging, boosting, tree based methods	[B1 sc1.6, 14.1-14.4; B2 sc9.2] [ obligatory assignment 2 ] [ exercises S10 ]

[B1] C.M. Bishop. Pattern recognition and Machine Learning Springer, 2006
[B2] T. Hastie, R. Tibshirani and J.H. Friedman. Elements of Statistical Learning: data mining, inference and prediction. Springer Verlag, Berlin, Germany, 2001

[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. McGraw-Hill, 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.

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, 9.00-12.00, U49
Reexam: January 5, 2012
Syllabus

Author: Marco Chiarandini <marco@imada.sdu.dk>

Date: 2011-07-29 11:18:36 CEST

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