Schedule
Spring 2013, third quarter, weeks 5-11 | Monday 08:00-10:00 in IMADA Seminarrum |
First lecture: January 28, 2013. | Wednesday 16:00-18:00 in U49 |
Last lecture: March 15, 2013. | Friday 08:00-10:00 in IMADA Seminarrum |
Lectures
(see also syllabus below for a detailed list of contents)
Lec. | Date | Topic | Literature and Assignments |
L0 | 04.11.2012 | Presentation | |
L1 | 28.01.2013 | Introduction, linear regression, k-nearest neighbor | [B2 1-2.4; B10; B6 5.1-5.10; A0] [ exercises ] |
L2 | 30.01.2013 | Linear models and probability interpretation | [B1 sc1.1-1.4, 2.3.6, 3.1, 3.3; B10] [ exercises ] |
L3 | 01.02.2013 | Binary variables, logistic regression, model assessment | [B2 sc 2.1; B10; B2 sc 1.5.5, 3.2] [ exercises ] |
E1 | 04.02.2013 | Exercises: solutions practical part, solutions theoretical part | [B2 sc. 2.2, 3.3] |
L4 | 06.02.2013 | Empirical model assessment, Generalized linear models | [B2 sc7.1-7.3, 7.10-7.11; B1 sc2.4; B10] [ exer. / sol ] |
L5 | 08.02.2013 | Perceptron, multi-layer perceptron, neural networks | [B1 sc1.5, 4.0, 4.1.1, 4.1.7, 5.1, 5.2, 5.3] [ exercises ] |
L6 | 11.02.2013 | Neural networks | [B1 sc5.2, 5.3, 5.5; B2 ch11] [ exercises / sol ] |
E2 | 13.02.2013 | Exercises | [ Assignment 1 ] |
L7 | 15.02.2013 | Gaussian discriminant analysis, naive Bayes | [B10; B1 sc4.2; B2 4.1-4.3] [ exercises / sol ] |
L8 | 18.02.2013 | Support vector machines | [B10; B1 sc7.1, AppE; B2 4.5; A3] [ exercises / sol ] |
L9 | 20.02.2013 | SVM and Kernel Methods | [B10; B1 6.1, 6.2; B2 sc2.8.2, ch6, sc12.1-12.3.5; B11] |
E3 | 22.02.2013 | Exercises | [ exercises / sol ] |
L10 | 25.02.2013 | Application Guidelines, Feature Selection, Bagging, Boosting | [B10, B12, B1 sc 14.1, 14.3] |
L11 | 27.02.2013 | Probabilistic graphical models | [B1 sc 8.1; B13] [ exercises / sol ] [ Assignment 2 / sol ] |
L12 | 01.03.2013 | Probabilistic graphical models | [B1 ch 8; L11] [ exercises / sol ] |
E4 | 04.03.2013 | Exercises | |
L13 | 06.03.2013 | Mixtures models, k-means, EM algorithm | [B10; B1 sc 9.1, 9.2; L10] [ exercises / sol ] |
E5 | 08.03.2013 | Exercises | |
L14 | 11.03.2013 | Tree based methods, Principal component analysis | [B1, sc 14.4, 12.1; B10; B2, sc 9.2, 14.5.1] |
E6 | 13.03.2013 | Exercises | [ exercises / sol ] |
E7 | 15.03.2013 | Exercises | |
E8 | 03.04.2013 | Question time at 9:00 in U49D | |
Literature
Other references
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[B2] T. Hastie, R. Tibshirani and J.H. Friedman. Elements of Statistical Learning: data mining, inference and prediction. Springer
Verlag, Berlin, Germany, 2001
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[B3] S. Marsland. Machine Learning: An Algorithmic Perspective. CRC Press,
Taylor and Francis group, 2009
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[B4] S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 2010.
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[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
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[B7] W.N. Venables and B.D. Ripley. Modern Applied Statistics with S. Springer, Fourth Edition. 2002.
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[B8] F.V. Jensen and T.D. Nielsen. Bayesian Networks and Decision Graphs. Springer New York, 2007.
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[B10] Lecture Notes by Andrew Nag, Stanford University
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[B11] Platt, John (1998), Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines
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[B12] P. Domingos. A few useful things to know about machine learning. Commun. ACM, ACM, 2012, 55(10) , 78-87
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[B13] A. Darwiche. Bayesian networks. Commun. ACM, ACM, 2010, 53(12), 80-90
Assessment
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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: April 4, 2013, 10-13 in U49, U49B
Exam instructions, Answer Templates [ Latex | Word | OpenDocument ]
Solutions
-
Reexam: June 12, 2013
-
Syllabus
-
Exam 2010
Author: Marco Chiarandini
<marco@imada.sdu.dk>
Date: 2013-05-01 17:30:57 CEST
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