phy694 Machine Learning II (Complete module description)

Attendance: 56 hrs, Self study: 124 hrs

Basic knowledge in higher Mathematics taught as part of first degrees in Physics, Mathematics, Statistics, Engineering or Computer Science (basic linear algebra and analysis) is required. Additionally, programming skills are required (Matlab or python).

The students will deepen their knowledge on mathematical models of data and sensory signals. Building upon the previously acquired Machine Learning models and methods, the students will be lead closer to current research topics and will learn about models that currently represent the state-of-the-art. Based on these models, the students will be exposed to the typical theoretical and practical challenges in the development of current Machine Learning algorithms. Typical challenges are analytical and computational
intractabilities, or local optima problems. Based on concrete examples, the students will learn how to address such problems. Applications to di erent data will teach skills to use the appropriate model for a desired task and the ability to interpret an algorithm's result as well as ways for further improvements. Furthermore, the students will learn interpretations of biological and arti cial intelligence based on state-of-the-art Machine Learning models.

This course builds up on the basic models and methods introduced in introductory Machine Learning lectures. Advanced Machine Learning models will be introduced alongside methods for ecient parameter optimization. Analytical approximations for computationally intractable models will be de ned and discussed as well as stochastic (Monte Carlo) approximations. Advantages of di erent approximations
will be contrasted with their potential disadvantages. Advanced models in the lecture will include models
for clustering, classi cation, recognition, denoising, compression, dimensionality reduction, deep learning, tracking etc. Typical application domains will be general pattern recognition, computational neuroscience and sensory data models including computer hearing and computer vision.

• Pattern Recognition and Machine Learning, C. M. Bishop, Springer 2006. (best suited for lecture).;
• Information Theory, Inference, and Learning Algorithms, D. MacKay, Cambridge University Press, 2003. (free online)

Klausur