252-0526-00L Statistical Learning Theory
|Semester||Spring Semester 2021|
|Lecturers||J. M. Buhmann, C. Cotrini Jimenez|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|252-0526-00 V||Statistical Learning Theory||3 hrs|
|J. M. Buhmann, C. Cotrini Jimenez|
|252-0526-00 U||Statistical Learning Theory||2 hrs|
|J. M. Buhmann, C. Cotrini Jimenez|
|252-0526-00 A||Statistical Learning Theory||2 hrs||J. M. Buhmann, C. Cotrini Jimenez|
|Abstract||The course covers advanced methods of statistical learning: |
- Variational methods and optimization.
- Deterministic annealing.
- Clustering for diverse types of data.
- Model validation by information theory.
|Objective||The course surveys recent methods of statistical learning. The fundamentals of machine learning, as presented in the courses "Introduction to Machine Learning" and "Advanced Machine Learning", are expanded from the perspective of statistical learning.|
|Content||- Variational methods and optimization. We consider optimization approaches for problems where the optimizer is a probability distribution. We will discuss concepts like maximum entropy, information bottleneck, and deterministic annealing.|
- Clustering. This is the problem of sorting data into groups without using training samples. We discuss alternative notions of "similarity" between data points and adequate optimization procedures.
- Model selection and validation. This refers to the question of how complex the chosen model should be. In particular, we present an information theoretic approach for model validation.
- Statistical physics models. We discuss approaches for approximately optimizing large systems, which originate in statistical physics (free energy minimization applied to spin glasses and other models). We also study sampling methods based on these models.
|Lecture notes||A draft of a script will be provided. Lecture slides will be made available.|
|Literature||Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001.|
L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
|Prerequisites / Notice||Knowledge of machine learning (introduction to machine learning and/or advanced machine learning)|
Basic knowledge of statistics.
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||8 credits|
|Examiners||J. M. Buhmann, C. Cotrini Jimenez|
|Language of examination||English|
|Repetition||The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.|
|Mode of examination||written 180 minutes|
|Additional information on mode of examination||70% session examination, 30% project; the final grade will be calculated as weighted average of both these elements. As a compulsory continuous performance assessment task, the project must be passed on its own and has a bonus/penalty function.|
The practical projects are an integral part (60 hours of work, 2 credits) of the course. Participation is mandatory. Failing the project results in a failing grade for the overall course examination.
Students who fail to fulfil the project requirement must de-register from the exam. Otherwise, they are not admitted to the exam and they will be treated as a no show.
|Written aids||4 A4 handwritten or fontsize 12 pages (2 sheets with notes on its two sides); course script.|
|This information can be updated until the beginning of the semester; information on the examination timetable is binding.|
|Recording||Statistical Learning Theory recorings|
|Only public learning materials are listed.|
|No information on groups available.|
|There are no additional restrictions for the registration.|