Dr. Bogdan Savchynskyy, WiSe 2019/20
Information for the exam
There will be an oral exam. To get an appointment for it write me an email with your time restrictions.
Summary
The course presents various existing optimization techniques for such important machine learning tasks, as inference and learning for graphical models and neural networks. In particular, it addresses such topics as combinatorial algorithms, integer linear programs, scalable convex and non-convex optimization and convex duality theory. Graphical models and neural networks play a role of working examples along the course. The goal of the course is to give a strong background for analysis of existing, and development of new scalable optimization techniques for machine learning problems.
Schedule and Information
The lectures and exercises will be given in English.
Venue: Mathematikon B: Berliner Str. 43, 3rd floor, seminar room B128.
Simply ring the bell "HCI am IWR" to open the front door.
- Lecture: Tue, 14:00 – 16:00, Mathematikon B, Berliner Str. 43, SR B128, the first lecture will be given on October 15
- Lecture: Wed, 14:00 – 16:00, Mathematikon B, Berliner Str. 43, SR B128
- Exercises: Wed, 09:00 – 11:00, Raum: Mathematikon B, Berliner Str. 43, SR B128, the first exercise will take place on October 23
Contact for lectures: Dr. Bogdan Savchynskyy
Contact for exercises: Stefan Haller
The seminar Combinatorial Optimization in Computer Vision and Machine Learning complements this lecture by taking a closer look at recent results and developments. We highly recommend it to all students interested in the topic.
Registration
Please register for the course in Müsli.
Course Material and Exercises
The course material is available here.
The deadline for submission of solutions for 3rd exercise sheet is 2020-01-14.
The deadline for submission of solutions for 4th exercise sheet is 2020-02-04.
(Topics for last exercise at 2020-02-05: Presentation solutions for 4th exercise sheet and preparation for oral examination.)
Table of Contents
I Inference in Graphical Models
- Acyclic Graphical Models. Dynamic Programming
- Background: Basics of Linear Programs and Their Geometry
- Inference in Graphical Models as Integer Linear Program
- Background: Basics of Convex Analysis and Convex Duality
- Duality of the LP Relaxation of Inference Problem
- Background: Basics of Convex Optimization
- Sub-Gradient and Block-Coordinate Ascent for Inference in Graphical Models
- Lagrangian (Dual) Decomposition
- Min-Cut/Max-Flow Based Inference
- LP Relaxation of Inference Problem as st-Min-Cut Problem
- Summary: Inference Algorithm Selection
II Joint Learning of Graphical Models and Neural Networks
- Structured Risk Minimization for Graphical Models
- CRF+CNN Models: Joint Training of Graphical Models and Neural Networks.
Literature
Text-book: B. Savchynskyy. Discrete Graphical Models - An Optimization Perspective