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Practical Course: Creation of Deep Learning Methods (10 ECTS)

Summer Semester 2021, TU München

Please send applications (including learning goals, programming skills description, code, grade transcripts - see preliminary meeting slides) to create-dl[at]vision.in.tum.de

Organizers: Vladimir Golkov, Prof. Dr. Daniel Cremers

Preliminary meeting (not obligatory): 1 February 2021 at 11am online: https://bbb.in.tum.de/vla-493-ke7
Slides from last semester's preliminary meeting are available here.

Sending an email to create-dl[at]vision.in.tum.de with sufficient info about yourself (learning goals, programming skills description, code, all grade transcripts) within the next few days is crucial for matching success. Details about the matching system can be found here and here.

If you ask for a spot after the matching phase, but do not hear from us soon, it means that we cannot offer you a spot.

Content

Using deep learning to solve real problems often requires the creation of novel appropriate deep learning methods, rather than just out-of-the-box usage of existing architectures. In this practical course, students will choose real open problems and learn how to analyze them, how to identify the requirements that a deep learning method should fulfill, and how to create novel deep learning methods that fulfill these requirements.

Some of the projects that can be chosen also include the analysis of design principles of existing methods, and subsequent usage of these design principles to create new methods.

If you want to propose an own project instead of choosing from the projects that we will offer, please discuss with us before 16 February 2021.

Prerequisites

Good programming skills. Eagerness to acquire and deepen knowledge about how to solve complex problems with machine learning. Passion for mathematics. Knowledge of Python and array programming in NumPy (or Matlab or similar) is recommended. Having good soft skills (or the willingness to acquire them quickly) and using them is a prerequisite, particularly good and strategic communication skills, ability to identify what is unclear, to formulate questions precisely.

Course Structure

In the first three weeks, there will be lectures every week, focusing on theoretical and practical concepts related to deep learning. During the semester, the students will work in groups and individually on practical deep learning projects. At the end of the project, each group will present their project with a following Q&A session. There will be no additional written or oral exam. Both the theoretical and practical part of the project will be considered in the final grading.

Course Schedule

There will be three lectures in the beginning of the semester.
Time: Thursdays 2-4pm
Online link (BBB): see email

Lecture 1: Machine Learning; Artificial Neural Networks; Convolutional Neural Networks; Q&A about Deep Learning
Lecture 2: Recap; Network Architecture Design; Q&A about Deep Learning
Lecture 3: Recap; Network Training; Understanding and Visualizing; Evaluating; Q&A about Deep Learning

Note: ECTS credits are the measure of workload. So-called semester weekly hours (Semesterwochenstunden, SWS) are NOT a measure of project work time, but merely of classroom time.

Literature

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Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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