392186 Vertiefung Neuronale Netze (V) (WiSe 2023/2024)
Contents, comment
-
This lecture will consider various unsupervised and instance-based learning approaches before turning back to deep neural networks. In the exercises will replicate various classical applications of neural network methods to interesting real-world problems.
- Topological Maps: Self-Organizing Maps, Growing Neural Gas, Hyperbolic SOM, Local Linear Maps
- Mixture Models: Gaussian Mixture Models, Gaussian Mixture Regression, Gaussian Processes
- Recurrent Neural Nets: Dynamics, Stability, applications: Hopfield net, CLM, ...
- Graph Networks: Processing Structural Information
- Generative Models: Generative Adversarial Networks (GAN), Actor-Critic, Variational Autoencoders
- Deep Reinforcement Learning
The lecture will be given in English language if desired by the audience.
Slides will be in English in any case.
Requirements for participation, required level
-
recommended prerequisites:
- Machine Learning basics
- Neural Networks basics
External comments page
Teaching staff
Dates ( Calendar view )
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| weekly | Mi | 8-10 | X-E0-226 | 09.10.2023-02.02.2024
not on: 11/1/23 / 12/27/23 / 1/3/24 |
Subject assignments
| Module | Course | Requirements | |
|---|---|---|---|
| 39-M-Inf-AI-adv-foc_ver1 Advanced Artificial Intelligence (focus) | Advanced Artificial Intelligence (focus): Vorlesung | Graded examination
|
Student information |
| 39-M-Inf-VNN Vertiefung Neuronale Netze | Vertiefung Neuronale Netze | Ungraded examination
Graded examination |
Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
- E-Learning Space
- E-Learning Space
- Moodle Courses
- Moodle Courses
- Address:
- WS2023_392186@ekvv.uni-bielefeld.de
- This address can be used by teaching staff, their secretary's offices as well as the individuals in charge of course data maintenance to send emails to the course participants. IMPORTANT: All sent emails must be activated. Wait for the activation email and follow the instructions given there.
- If the reference number is used for several courses in the course of the semester, use the following alternative address to reach the participants of exactly this: VST_431557575@ekvv.uni-bielefeld.de
- Notes:
- Additional notes on the electronic mailing lists
- Last update basic details/teaching staff:
- Wednesday, January 17, 2024
- Last update times:
- Friday, August 11, 2023
- Last update rooms:
- Friday, August 11, 2023
- Type(s) / SWS (hours per week per semester)
- lecture (V) / 2
- Language
- This lecture is taught in english
- Department
- Faculty of Technology
- Questions or corrections?
- Questions or correction requests for this course?
- Planning support
- Clashing dates for this course
- Links to this course
- If you want to set links to this course page, please use one of the following links. Do not use the link shown in your browser!
- The following link includes the course ID and is always unique:
- https://ekvv.uni-bielefeld.de/kvv_publ/publ/vd?id=431557575
- Send page to mobile
-
Click to open QR code
Scan QR code:
- ID
- 431557575