Machine Learning: Methods and Tools
Module Number: EI71040
Duration: 1 semester
Occurence: Winter semester
Number of ECTS: 5
Professor in charge: Wille
Amount of work
Contact hours: 60
Self-study hours: 90
Description of achievement and assessment methods
The examination consists of 2 parts: - a practical part (50%) and an - oral examination (50%) at the end of the course time. The practical examination takes place in a computer lab at TUM. The students have three hours to solve a practical design problem in the area of microelectronics with the methods of Machine Learning. The oral examination consists of sessions of 30 minutes, two students will be examined in one session. The oral examination comprises
** comprehension questions to check the knowledge,
** hand calculation tasks to check the solving skills,
** questions on the practical exercises and
** background questions.
Knowledge in Linear Algebra, Analysis and Statistics, Proficiency in a programming language, e.g., C, C++, Java
**Lecture, Exercises and hands-on lab** 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;
After participating in the module, the student knows and masters in a differentiated way basic methods and algorithms of machine learning. He/she is able to apply them to engineering in microelectronic design tasks. In addition, he/she is able to become familiar with other areas of machine learning. He/she knows the embeddedness of Machine Learning in the digital transformation and is aware of the social opportunities and risks.
Lecture and exercise are designed as interactive frontal lessons. By projection of slides and blackboards, the algorithms to be taught are developed step by step and with the participation of the learners in the lecture. In the exercise, work instruction takes place by examples and joint calculation of tasks. Algorithms are used as examples and repeatedly. 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.
* Maschinelles Lernen - Grundlagen und Algorithmen in Python, Frochte J., Hanser Fachbuch
* 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 Responsible for