Carlos Cotrini Jimenez: Katalogdaten im Frühjahrssemester 2020
|Name||Herr Dr. Carlos Cotrini Jimenez|
ETH Zürich, UNG F 14
|252-0526-00L||Statistical Learning Theory||7 KP||3V + 2U + 1A||J. M. Buhmann, C. Cotrini Jimenez|
|Kurzbeschreibung||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.
|Lernziel||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.|
|Inhalt||- 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.
|Skript||A draft of a script will be provided. Lecture slides will be made available.|
|Literatur||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
|Voraussetzungen / Besonderes||Knowledge of machine learning (introduction to machine learning and/or advanced machine learning)|
Basic knowledge of statistics.