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EECS738 Xue-wen Chen EECS 738: Machine Learning Fall 2011, Prof. Xue-wen Chen The University of Kansas.

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Presentation on theme: "EECS738 Xue-wen Chen EECS 738: Machine Learning Fall 2011, Prof. Xue-wen Chen The University of Kansas."— Presentation transcript:

1 EECS738 Xue-wen Chen EECS 738: Machine Learning Fall 2011, Prof. Xue-wen Chen The University of Kansas

2 EECS738 Xue-wen Chen Machine Learning Predict the unknown from uncertain information

3 EECS738 Xue-wen Chen Why Machine Learning?

4 EECS738 Xue-wen Chen Speech Recognition Hidden Markov models and their generalizations

5 EECS738 Xue-wen Chen Tracking and Robot Localization [Fox et al.] [Funiak et al.] Kalman Filters

6 EECS738 Xue-wen Chen Evolutionary Biology [Friedman et al.] Bayesian networks, Sequence alignment …

7 EECS738 Xue-wen Chen Modeling Sensor Data Undirected graphical models [Guestrin et al.]

8 EECS738 Xue-wen Chen Planning Under Uncertainty F E G P Peasant Footman Enemy Gold R tt+1 Time A Peasant A Build A Footman P(F|F,G,A B,A F ) [Guestrin et al.] Dynamic Bayesian networks Factored Markov decision problems

9 EECS738 Xue-wen Chen Images and Text Data [Barnard et al.] Hierarchical Bayesian models

10 EECS738 Xue-wen Chen Structured Data (text, webpage,…) [Koller et al.] Probabilistic relational models

11 EECS738 Xue-wen Chen And many many more…

12 EECS738 Xue-wen Chen Syllabus About me the course (see the syllabus) Covers a wide range of machine learning topics (if time permits): from basic to state-of-the-art –Fundamentals –Supervised and unsupervised –SVM, NN, DTs –Bayesian networks –MCMC, Gibbs, EM –Gaussian and hybrid models, discrete and continuous variables –temporal and template models, hidden Markov Models, –Forwards-Backwards, Viterbi, Baum-Welch, Kalman filter, Covers algorithms, theory and application

13 EECS738 Xue-wen Chen Prerequisites Mathematical maturity: –Vector/Matrix –Probabilities: distributions, densities, marginalization… –Basic statistics: moments, typical distributions, regression… –Optimization –Ability to deal with abstract mathematical concepts Programming –Experienced in at least one language (C, C++, Java, R, Matlab …) Its going to be fun and hard work –Think before you decide: for credit only or for learning something –The class will be fast paced –Willing to spending time and efforts (in classroom and out…) –Dealing with mathematical formulas … CANNOT emphasize it more –Understand it, program it –Fun only if you enjoy it …

14 EECS738 Xue-wen Chen Text Books –Machine Learning: an algorithm perspective, Stephen Marsland, CRC Press. –(optional) Tom Mitchell. Machine Learning, 1997, WCB/McGraw-Hill. –(optional) Pattern Recognition and Machine Learning, Christopher Bishop, Springer –Additional handouts will be provided as needed.

15 EECS738 Xue-wen Chen Grades Exam: 40% Final Project: 60% Participation10% The cutoffs for grades will be roughly as follows: A: 90 – 100 B: 80 – 89 C: 70 – 79 D: 60 – 69 F: 0 – 59

16 EECS738 Xue-wen Chen Exam To test –if you are ready!! –If you will survive Include but not limited to –Linear algebra –Matrix calculus –Probability ad Statistics –Optimization

17 EECS738 Xue-wen Chen Project Choose a topic that is related to your research interest and pertains to the course material. The proposal should include the following sessions (Due: October 24) –the project goal, –the problems to be studied, –overview of current methods, –proposed methods, –expected results, and –references (about 4 pages: single space, fond size = 12, references are not counted). References should be cited in the proposal. A written final report in the style of a journal article is also required. Final project is due by Dec. 07 (no late written project). Each student will give classroom presentation about the final project. Details: see the syllabus

18 EECS738 Xue-wen Chen Some Important Dates October 24 – Project Proposal Due December 07 – Final project (written) due

19 EECS738 Xue-wen Chen Tentative Lectures See syllabus Preliminaries: –matrix, statistics, optimization Questions?

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