Introduction to Deep Learning
General Information
Summer Semester 2021.
SWS: 3, ECTS: 5.
Available for master students from: Computational Science and Engineering, Informationssystemtechnik, Elektrotechnik, Communications Technology.
The course will be conducted in English.
The material of the course and all imporant infromation is available at https://moodle.uni-ulm.de/ .
Important: The course will take place electronically in the summer semester 2021. Please check on Moodle for instructions.
Objectives
The aim of the course is to acquire basic knowledge on deep learning. This includes classical neural network models and recent deep architectures. Topics such as convolutional neural networks, optimisation, regularisation, generative models, sequential models will be covered among others. In the exercise, the participants will implement some of the standard models for classification or regression, transfer learning, generative models and acquire knowledge on machine learning applications.
Participation
Background on linear algebra, calculus, optimisation and programming will be helpful.
There will be two hours lecture every week and two hour exercise almost every second week.
The registration is performed through https://campusonline.uni-ulm.de.
For further questions on the course, the students may contact the lecturer.
Exam: End of July / Beggining of August 2021.
Repetition Exam: September 2021.
Lecture Schedule
| Date | Time | Room | Topic | Comments |
|---|---|---|---|---|
| 19.04.2021 | 12:00 | Online | Introduction |
|
| 26.04.2021 | 12:00 | Online | Machine learning basics (Part 1) | |
| 03.05.2021 | 12:00 | Online | Machine learning basics (Part 2) | |
| 10.05.2021 | 12:00 | Online | Feed-forward Networks and Backpropagation | |
| 17.05.2021 | 12:00 | Online | Optimization | |
| 24.05.2021 | - | - | Bank Holiday | |
| 31.05.2021 | 12:00 | Online | Regularization, Parameter Initialization, Normalization (Part 1) | |
| 07.06.2021 | 12:00 | Online | Regularization, Parameter Initialization, Normalization (Part 2) | |
| 14.06.2021 | 12:00 | Online | Convolutional Neural Networks | |
| 21.06.2021 | 12:00 | Online | Modern Deep Architectures | |
| 28.06.2021 | 12:00 | Online | AutoEncoders | |
| 05.07.2021 | 12:00 | Online | Sequential Processing, Attention (Part 1) | |
| 12.07.2021 | 12:00 | Online | Sequential Processing, Attention (Part 2) | |
| 19.07.2021 | 12:00 | Online | Generative Models | |
| 26.07.2021 | 12:00 | Online | Recap, Exam Q&A | |
Exercise Schedule
| Date | Time | Room | Topic | Comments |
|---|---|---|---|---|
| 05.05.2021 | 14:00 | Online | Machine learning basics | Zoom Q&A about exercise. Actual exercise is released two days earlier. |
| 19.05.2021 | 14:00 | Online | Feed-forward Networks and Backpropagation | Zoom Q&A about exercise. Actual exercise is released two days earlier. |
| 02.06.2021 | 14:00 | Online | Optimization | Zoom Q&A about exercise. Actual exercise is released two days earlier. |
| 16.06.2021 | 14:00 | Online | Regularization, Parameter Initialization, Normalization | Zoom Q&A about exercise. Actual exercise is released two days earlier. |
| 23.06.2021 | 14:00 | Online | Convolutional Neural Networks | Zoom Q&A about exercise. Actual exercise is released two days earlier. |
| 07.07.2021 | 14:00 | Online | Autoencoders | Zoom Q&A about exercise. Actual exercise is released two days earlier. |
| 21.07.2021 | 14:00 | Online | Sequential Processing | Zoom Q&A about exercise. Actual exercise is released two days earlier. |
Lecturer
Exercise
Schedule and Place
Lecture: Monday, 12:00 - 14:00 Uhr (Online)
Exercise: Monday (every second week), 14:00 - 16:00 Uhr (Online)