Neural Networks: Theory and Implementation

This is is the version for CS, DSAI, VC, Bioinformatics, Systems Engineering, Physics, wirtschaftsinformatik, etc.

Starts: 20.10.26 14:15

Location: large lecture hall mathematics, HS1 in E25

Exam: 9.2.27 (location will follow a few days before the exam)

Tutorials: tbd

Outline:

1. Linear Algebra and Principal Component Analysis (PCA)
2. Numerical Computation
3. Machine Learning Basics
4. Deep Feedforwad Neural Networks
5. Regularization for Deep Learning
6. Optimization for Deep Learning
7. Convolutional Neural Networks
8. Sequence Modelling: Recurrent and Recursive Neural Networks
9. Transformers
10. GNNs

Prerequisite: knowledge in basic machine learning (as acquired eg. in EML (https://cms.sic.saarland/eml25/)) 

Text Books:

Neural Networks and Deep Learning by Charu C. Aggarwal

Deep Learning by Aaron Courville, Ian Goodfellow, Joshua Bengio

Geometry of Deep Learning, Jong Chul Ye

Deep Learning Architectures: A Mathematical Approach, Ovidiu Calin