Einführung in das Probabilistische Maschinelle Lernen (Sommersemester 2024)

Lecturer: Prof. Dr. Ralf Herbrich (Artificial Intelligence and Sustainability) , Dr. Rainer Schlosser (Enterprise Platform and Integration Concepts)

General Information

Weekly Hours: 4
Credits: 6
Graded: yes
Enrolment Deadline: 01.04.2024 - 30.04.2024
Examination time §9 (4) BAMA-O: 24.07.2024
Teaching Form: Lecture / Exercise
Enrolment Type: Compulsory Elective Module
Course Language: English

Programs, Module Groups & Modules

IT-Systems Engineering BA

BPET: Business Process & Enterprise Technologies
- HPI-BPET-G Grundlagen
BPET: Business Process & Enterprise Technologies
- HPI-BPET-V Vertiefung
HCGT: Human Computer Interaction & Computer Graphics Technology
- HPI-HCGT-G Grundlagen
HCGT: Human Computer Interaction & Computer Graphics Technology
- HPI-HCGT-V Vertiefung
ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-G Grundlagen
ISAE: Internet, Security & Algorithm Engineering
- HPI-ISAE-V Vertiefung
OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-G Grundlagen
OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-V Vertiefung

Description

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 relationships of machine learning models and explainable artificial intelligence. This 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. In the course, we will implement all the inference techniques and apply them to real-world problems.

Requirements

It is highly recommended that you have completed the following courses

HPI-MA3: Stochastik (BSc studies)
HPI-MA2: Analysis und Lineare Algebra (BSc studies)

Literature

Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer.
MacKay, D. J.C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge University Press.
Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
Ross, S. (2021). Introduction to Probability and Statistics for Engineers and Scientists. Academic Press.

Learning

We will have one weekly lecture and one weekly tutorial.

Lecture: Monday, 11:00 - 12:30 (HS 1)
Tutorial: Tuesday, 15:15 - 16:45 (HS 1)

HPI Moodle Course: https://moodle.hpi.de/course/view.php?id=755

Examination

There will be three exercises throughout the semester counting for 30% of the points. The remaining 70% of points will be awarded through a written exam at the end of the course.

Dates

Week 1 (8.4 & 9.4): History & Probability Theory
Week 2 (15.4 & 16.4): Inference & Decision Making
Week 3 (22.4 & 23.4): Graphical Models: Independence
Week 4 (29.4 & 30.4): Graphical Models: Inference
Week 5 (6.5 & 7.5): Bayesian Ranking
Week 6 (13.5 & 14.5): Linear Basis Function Models
Week (20.5 & 21.5): no lectures
Week 7 (27.5 & 28.5): Bayesian Regression
Week 8 (3.6 & 4.6): Baysian Classification
Week 9 (10.6 & 11.6): Non-Baysian Classification Learning
Week 10 (17.6 & 18.6): Gaussian Processes
Week 11 (24.6 & 25.6): Information Theory
Week 12 (1.7 & 2.7): Real-World Applications of Probabilistic Machine Learning
Week 13 (8.7 & 9.7): Exam Preparation

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