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252-0535-00L 10 Credits BSC , DZ , DR , SHE , MSC , WBZ D-BSSE , D-INFK , D-MATH , D-PHYS , D-ITET , D-MAVT , D-ERDW , D-GESS
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Advanced Machine Learning

Lecturers & Examiners: Prof. em. Dr. Joachim M. Buhmann, Dr. Carlos Cotrini Jimenez
VVZ CR 3.09

Last Updated: 2026-06-01 11:30:46

Abstract

Machine 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 accompanied by practical machine learning projects.

Objective

Students will be familiarized with advanced 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 modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.

Content

The 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: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems

Resources

Lecture Notes

No lecture notes, but slides will be made available on the course webpage.

Literature

C. 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.

General Information

Language
English
Levels
BSC , DZ , DR , SHE , MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 180 minutes
Aids
Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.
The practical projects are an integral part of the course (60 hours of work, 2 credits). Participation is mandatory. A passing grade for the practical projects is mandatory for admission to the examination of the course.Students who successfully finished the practical projects, will receive a final grade for the course that is calculated as a weighted average of the grade achieved in the written examination (70%) and the grade achieved in the practical projects (30%).Students who achieve a failing grade in the practical projects have to de-register from the exam. Otherwise, they will not be admitted to the examination resulting in a no-show marking.The examination might take place at a computer.

Course Components

Type Title Time & Place Hours
lecture Advanced Machine Learning
Montag 10-12 HG F7 mit Videoübertragung ins HG F5.
  • Mon 10:15-12:00 (HG F 5)
  • Mon 10:15-12:00 (HG F 7)
  • Thu 15:15-16:00 (ETA F 5)
3 h weekly
exercise Advanced Machine Learning
Tutorials: Please attend only the tutorial assigned to you by the first letter of your surname. We do not attend requests to change tutorials. Wed 14-16 - CAB G 61 → surname A-L Fri 14-16 - CHN C 14 → surname M-Z All tutorial sessions are identical.
  • Wed 14:15-16:00 (CAB G 61)
  • Fri 14:15-16:00 (CHN C 14)
2 h weekly
independent project Advanced Machine Learning
Project Work, no fixed presence required.
No time listed 4 h weekly

Offered In