Machine Learning: Methods and Tools
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2018/9
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|SS 2022||WS 2019/20||WS 2018/9|
EI71040 is a semester module in English language at Master’s level which is offered every semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|150 h||60 h||5 CP|
Content, Learning Outcome and Preconditions
Digital transformation and machine learning;
Python, standard libraries, SciPY and NumPy;
Theory of Machine Learning, Regularization, Errors and Noise;
Data analysis, pre-processing, visualization:
Introduction to algorithms of Machine Learning;
Introduction to Feedforward Neural Networks and Convolutional Neural Networks, RNNs, LSTM;
Training of neural networks, attention models, unsupervised learning, reinforcement learning, hyper-parameter optimization;
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VI||4||Machine Learning: Methods and Tools||Servadei, L. Wille, R.||
Learning and Teaching Methods
The students prepare for the lecture and exercise by studying the documents and prepare the material they have taken through self-study. Own literature searches are part of self-study.
In a hands-on lab part, the students are given practical problems in the field of microelectronics for independent solving. The tasks set include practical applications of machine learning in the automatic design of integrated circuits and systems.
* Einführung in Python 3, Klein B., Hanser Fachbuch.
* Learning from Data, Abu-Mostafa, Yaser S. et al., AMLBook 2012.
* Deep Learning, Goodfellow, Ian et al, The MIT Press 2016.
* Machine learning: A probabilistic perspective, Murphy, Kevin P., The MIT Press 2013
Description of exams and course work
The understanding and knowledge in the field of machine learning is examined. Furthermore, the ability to use machine learning methods to formulate and solve design problems in microelectronics will be tested on the basis of manual calculation tasks as well as tasks from the lecture-accompanying lab.
Finally, with background questions, the ability to solve further engineering tasks by means of machine learning is examined.
There is a possibility to take the exam in the following semester.