Time only SS
Time Thursday, 08.15h-11.30h
Room 220
Credits 2 SWS / 5 ECTS
Exam Lab Work


  • First lesson in term SS 24: 21.03.24

Prerequisites for participation

  • Successful completion of the CSM Machine Learning Lecture.

Structure, Contents, Documents

In this lab student-groups must implement selected applications of Machine Learning in particular Deep Learning. All applications must be implemented in Python Jupyter-Notebooks. The Python machine learning libraries scikit-learn, Tensorflow and Keras will be applied.

I recommend to download Anaconda for Python 3.8 or newer. Then a new virtual environment shall be created by conda create -n pia python=3.8 anaconda. In this virtual environment use pip install to install tensorflow, keras, gensim, and other required modules.

Each of the lab-exercises (applications) will be graded. The final grad of the course is the mean of the exercise grades.

New, from term SS21 on is the combination of the lectures PIA and Selected Topics of Deep Learning (STDL). Actually the lab exercises of PIA has been extended and partitioned into these two lectures. As depicted below the first 4 exercises are assigned to PIA, the remaining 4 exercises are assigned to STDL. The 2 modules therefore run consecutively. Students, which like to execute all exercises must register for PIA and STDL. It is also possible to register only for PIA. It is not possible to register only for STDL, because it makes no sense to skip the intro in the first part and start with the exercise.

PIA in Ilias

Lecture Contents
21.03.2024 Short Introduction and Registration
28.03.2024 Introduction into Tensorflow
04.04.2024 Implementation of GAN for MNIST
11.04.2024 Implementation of GAN for MNIST
18.04.2024 Word Embeddings Transformers
25.04.2024 Word Embeddings Transformers
02.05.2024 RNN Music Generation
09.05.2024 Christi Himmelfahrt
16.05.2024 RNN Music Generation
———— —————–
23.05.2024 Graph Neural Networks
30.05.2024 Fronleichnam
06.06.2024 RL 1: Dynamic Programming
13.06.2024 RL 2: Reinforcement Learning
20.06.2024 RL 3: Deep Reinforcement Learning
27.06.2024 RL 3: Deep Reinforcement Learning