252-0535-00L  Machine Learning

SemesterAutumn Semester 2013
LecturersA. Krause
Periodicityyearly course
Language of instructionEnglish



Catalogue data

AbstractMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is intended as an introduction, advanced topics will be discussed in “Statistical Learning Theory".
ObjectiveStudents will be familiarized with the most important concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to regularization.
ContentThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

- Bayesian theory of optimal decisions
- Maximum likelihood and Bayesian parameter inference
- Classification with discriminant functions: Perceptrons, Fisher's LDA and support vector machines (SVM)
- Ensemble methods: Bagging and Boosting
- Regression: least squares, ridge and LASSO penalization, non-linear regression and the bias-variance trade-off
- Non parametric density estimation: Parzen windows, nearest nieghbour
- Dimension reduction: principal component analysis (PCA) and beyond
Lecture notesNo lecture notes, but slides will be made available on the course webpage.
LiteratureC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Prerequisites / NoticeSolid basic knowledge in analysis, statistics and numerical methods for
CSE. Familiarity with Matlab for solving the programming exercises.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersA. Krause
Typesession examination
Language of examinationEnglish
Course attendance confirmation requiredNo
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Additional information on mode of examination70% schriftliche Sessionsprüfung, 30% Projekt
Written aidsTwo A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkInformation
Only public learning materials are listed.

Courses

NumberTitleHoursLecturers
252-0535-00 VMachine Learning3 hrs
Mon14-15CAB G 11 »
Tue10-12CAB G 11 »
A. Krause
252-0535-00 UMachine Learning2 hrs
Wed15-17CAB G 61 »
Thu15-17CAB G 59 »
Fri08-10CAB G 52 »
13-15CHN G 46 »
A. Krause

Restrictions

There are no additional restrictions for the registration.

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