252-0535-00L  Advanced Machine Learning

SemesterAutumn Semester 2023
LecturersJ. M. Buhmann
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
252-0535-00 VAdvanced Machine Learning
Freitag 8-10 HG F7 mit Videoübertragung ins HG F5.
3 hrs
Thu15:15-16:00ETA F 5 »
Fri08:15-10:00HG F 5 »
08:15-10:00HG F 7 »
J. M. Buhmann
252-0535-00 UAdvanced 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.
2 hrs
Wed14:15-16:00CAB G 61 »
Fri14:15-16:00CHN C 14 »
J. M. Buhmann
252-0535-00 AAdvanced Machine Learning
Project Work, no fixed presence required.
4 hrsJ. M. Buhmann

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 accompanied by practical machine learning projects.
ObjectiveStudents 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.
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:

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
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 / NoticeThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.

PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits10 credits
ExaminersJ. M. Buhmann
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 180 minutes
Additional information on mode of examinationThe practical projects are an integral part of the course (60 hours of work, 2 credits). Participation is mandatory. A failing grade for the practical projects will result in a failing grade for the course.

For students who obtain a passing grade for the practical projects, the final grade for the course will be 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 exam and will be treated as no-shows.

The exam might take place at a computer.
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.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

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