An Introduction to Probabilistic Machine Learning

Probabilistic machine learning has gained a lot of practical relevance over the past 15 years as it is highly data-efficient, allows practitioners to easily incorporate domain expertise and, due to the recent advances in efficient approximate inference, is highly scalable. Moreover, it has close relations to causal inference which is one of the key methods for measuring cause-effect relationship of machine learning models and explainable artificial intelligence. This openHPI course will introduce all recent developments in probabilistic modeling and inference. It will cover both the theoretical as well as practical and computational aspects of probabilistic machine learning.

自十月 18, 2023起开始自学
语言: English
Advanced, Big Data and AI

课程信息

Probabilistic machine learning has gained a lot of practical relevance over the past 15 years as it is highly data-efficient, allows practitioners to easily incorporate domain expertise and, due to the recent advances in efficient approximate inference, is highly scalable. Moreover, it has close relations to causal inference which is one of the key methods for measuring cause-effect relationship of machine learning models and explainable artificial intelligence. This openHPI course will introduce all recent developments in probabilistic modeling and inference. It will cover both the theoretical as well as practical and computational aspects of probabilistic machine learning.

This course requires Julia programming; we will use the CodeOcean feature of openHPI. We will also assume that the participants have a solid understanding of analysis and calculus.

LITERATURE

In this course, we will make use of the following four textbooks:

  1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  2. Koller, D., and N. Friedman. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
  3. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
  4. Ross, S. (2021). Introduction to Probability and Statistics for Engineers and Scientists. Academic Press.

课程内容

  • Week 1:

    This will will cover the following four topics: (1) What is Machine Learning, (2) The Role of Probability in Machine Learning, (3) Introduction to Probability Theory and (4) Probability Distributions.
  • Week 2:

    This week will cover the following three topics: (1) Graphical Models: Bayesian Networks, (2) Graphical Models: Factor Graphs and the Sum-Product Algorithm, and (3) Bayesian Ranking (TrueSkill).
  • Week 3:

    This week will cover the following two larger topics: (1) Bayesian Linear Regression, and (2) Gaussian Processes.
  • Week 4:

    This final week will cover these three topics: (1) Bayesian Classification algorithms, (2) Non-Bayesian Classification learning algorithms, and (3) modelling text and image data.
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该课程提供者

Prof. Dr. Ralf Herbrich

Prof. Dr. Ralf Herbrich is Full Professor at University of Potsdam and Head of the Chair for Artificial Intelligence and Sustainability at the Hasso Plattner Institute Potsdam. He has studied at Technical University of Berlin both for a Diploma degree in Computer Science (with focus on Computer Graphics and Artificial Intelligence (AI)) in 1997 and a PhD degree in Theoretical Statistics in 2000, respectively. Prof. Dr. Ralf Herbrich worked in both basic and applied science at Microsoft, Facebook, Amazon, and Zalando.

He does not accept existing scientific boundaries and thinks that the largest breakthroughs will be made at the intersection of existing disciplines. His research interests include approximate computing, Bayesian inference and decision making, game theory, information retrieval, natural language processing, computer vision, distributed systems, machine learning theory and knowledge representation and reasoning. Prof. Dr. Ralf Herbrich has published over 80 peer reviewed conference and journal papers in these fields.