Neural Networks: Theory and Implementation (NNTI, Winter 2024/2025)

This is is the version for CS, systems engineering, wirtschaftsinformatik, etc. No CoLi students permitted in this edition. The corresponding LSF entry .

Starts: 22.10.25

Location: HS1 in E25 (large lecture hall math)

Exam: tbd

Registration for participation: please register here

Registration for the exam has to be done separately. Please check LSF and the corresponding deadline.

Tutorials:

There will be four tutorials groups. Details to follow.

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

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