COURSE CANCELLED This course will introduce the basic concepts of probabilistic graphical models. Graphical Models are a unified framework that allow to express complex probability distributions in a compact and computationally tractable way. Many machine learning applications are tackled by the use of these models, in this course we will highlight the possibilities with applications mainly from natural language processing and computer vision, but potentially also from the life sciences.
The main goal of the class is to understand the concepts behind graphical models and to give hands-on knowledge such that one is able to design models for different applications. The lecture material is roughly divided into three parts: classical graphical models (model classes, factor graph representations, parameter learning, exact and approximate inference techniques), deep generative models (GANs, Variational Autoencoders), and practical applications (image denoising, topic modeling, image generation, ...).
The exercises will be a mix of theoretical and practical assignments.
Target group: Students interested in using probabilistic graphical models for their research.
Prerequisites: • strong quantitative background (linear algebra, calculus, probabilities)
• being able to read code in Python
• being able to program in a language that allows scientific numerical computing, ideally Python.
• having taken the statistical machine learning and/or DSSC core courses are a plus
Evaluation: homework
Teaching format: • video lectures from 2020 recording, interactive discussion meetings, homework
ECTS: 3 Year: 2021
Track segment(s):
CS-AI Computer Science - Artificial Intelligence
DSSC-PROB Data Science and Scientific Computing - Probabilistic Models
Teacher(s):
Paul Henderson Christoph Lampert
Teaching assistant(s):
If you want to enroll to this course, please click: REGISTER
- Trainer/in: Paul HENDERSON
- Trainer/in: Christoph Lampert