- © NI, TUB
This is the second of two consecutive courses on topics in machine learning and artificial neural networks. Areas to be covered:
(i) Probability Density Estimation
(ii) Projection Methods (PCA, ICA)
(iii) Stochastic Optimization
(iv) Clustering and Embedding (K-Means, SOM)
| Credits |
Time |
Lecturer |
Room |
Language |
| 2 SWS Lecture |
Thu 10-12 |
Prof. Dr. Klaus Obermayer |
MA 004 | English |
| 2 SWS Tutorial |
Thu 14-16 |
Timm Lochmann |
FR 3531 |
English |
| 2 SWS Tutorial |
Thu 16-18 |
Timm Lochmann |
FR 3531 |
English |
mehr zu: Machine Intelligence II
- © Mohr, NI, TUB
During this course, participants work on a scientific project of limited
scope under the supervision of an experienced researcher. Project topics
vary every semester, but are always related to the current research
projects of the Neural Information Processing Group. In the past, topics
were selected from research fields including the modeling of neural
systems, machine learning, artificial neural network and their application,
and from the analysis of neural data. During the course, students will
read original publications, learn how to prepare and present a brief
project proposal, learn how to scientifically address a complex problem,
learn how to discuss and defend their findings during a scientific poster
session, and a how to compile their results in form of a typical conference
paper. This course also includes a seminar part.
| Umfang | Zeit | Dozent | Ort
| Sprache |
| 6 SWS Projekt
| Di 12-14 & Do 12-14 | Prof. Dr. Klaus Obermayer & Mitarbeiter | FR2521 | deutsch & englisch |
mehr zu: Neuronale Netze
