Machine Learning: Methods and Tools

Lecturer (assistant)
Number0000003425
Type
Duration4 SWS
TermWintersemester 2020/21
Language of instructionEnglish
Position within curriculaSee TUMonline

Dates

Admission information

Objectives

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.

Description

In WS 2020/21 the course is planned to be presented online and asynchronous, i.e., download times of videos are at the discretion of participants. The exam is planned to be in written form and in physical presence (no warranty). **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;

Prerequisites

Knowledge in Linear Algebra, Analysis and Statistics, Proficiency in a programming language, e.g., C, C++, Java

Teaching and learning methods

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.

Examination

The exam is in written form with closed book policy and takes 60 minutes. 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.

Recommended literature

* 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

Links


All courses

Bachelorbereich: BSc-EI, MSE, BSEEIT

 

WS

SS

Diskrete Mathematik für Ingenieure (BSEI, EI00460)

Discrete Mathematics for Engineers (BSEEIT) (Schlichtmann) (Januar)

 

O

WS

SS

Entwurf digitaler Systeme mit VHDL u. System C (BSEI, EI0690) (Ecker)

WS 20/21 block course after lecture period

P

 

SS

Entwurfsverfahren für integrierte Schaltungen (MSE, EI43811) (Schlichtmann)

 

WS

 

Methoden der Unternehmensführung (BSEI, EI0481) (Weigel)

O

WS

 

Praktikum System- und Schaltungstechnik (BSEI, EI0664) (Schlichtmann et al.)

?

 

SS

Schaltungssimulation (BSEI, EI06691) (Gräb/Schlichtmann)

 

 

Masterbereich: MSc-EI, MSCE, ICD

 

SS

Advanced Topics in Communication Electronics (WS20/21: Willy Sansen) (MSCE, MSEI, EI79002)

P

WS

 

Aspects of Integrated Systems Technology & Design (MSCE, MSEI, EI5013) (Wurth)

fällt aus

WS

 

Electronic Design Automation (MSCE, MSEI, EI70610) (B. Li, Tseng)

O

WS

 

Design Methodology and Automation (ICD) (Schlichtmann) (Nov)

 

WS

SS

Machine Learning: Methods and Tools (MSCE, MSEI, EI71040) (Ecker)

O

WS

SS

SS

Mathematical Methods of Circuit Design (MSCE, MSEI, EI74042) (Gräb)

Simulation and Optimization of Analog Circuits (ICD) (Gräb) (Mai)

O+P

WS

 

Mixed Integer Programming and Graph Algorithms in Engineering Problems (MSCE, MSEI, EI71059) (Tseng)

O

WS

SS

Numerische Methoden der Elektrotechnik (MSEI, EI70440) (Diepold oder Schlichtmann)

O

WS

WS

SS

Seminar VLSI-Entwurfsverfahren (MSEI, EI7750) (Schlichtmann/Müller-Gritschneder)

Seminar on Topics in Electronic Design Automation (MSCE, EI77502) (Schlichtmann/Müller-Gritschneder)

O P?

O

WS

SS

Synthesis of Digital Systems (MSCE, MSEI, EI70640) (Müller-Gritschneder)

O

WS

 

Testing Digital Circuits (MSCE, MSEI, EI50141) (Otterstedt)

P

WS

 

Timing of Digital Circuits (MSCE, MSEI, EI70550) (B. Li, Zhang)

O

WS

SS

VHDL System Design Laboratory (MSCE, MSEI, EI7403) (Schlichtmann)

O

WS

SS

VLSI Design Laboratory (MSCE, MSEI, EI5043) (Schlichtmann)

fällt aus

 

The right-most column describes the planned type of lecture in the winter term 2020/21 - assuming that lecture halls are available: O=online, P=presence

 

MSE: Munich School of Engineering (TUM)
BSEEIT: Bachelor in Electrical Engineering and Information Technology (TUM-Asia)
ICD: Master of Science in Integrated Circuit Design (TUM-Asia)
MSCE: Master of Science in Communications Engineering (TUM)

MSEI: Master of Science in Elektrotechnik und Informationstechnik

BSEI: Bachelor of Science in Elektrotechnik und Informationstechnik

 

Please keep yourself updated at https://www.tum.de/die-tum/aktuelles/coronavirus/studium/ and www.ei.tum.de for updated information about teaching.