AV Artificial Intelligence

MW2378Master's Modules5 ECTSEnglishwinter semesterDepartment Mobility Systems Engineering

AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You will obtain a comprehensive overview of methods of artificial intelligence and machine learning with practical relevance to vehicle technology. In the end you will be able to select appropriate ML methods for various questions in vehicle technology and implement them in code (e.g., for autonomous driving tasks).

What you will be able to do

Overview of AI- and ML-methods
Selection of suitable ML methods for given problems
Implementation of ML methods in code (Python)
Application of the methods to vehicle technology problems (e.g., lane and vehicle detection)
Understanding the fundamental mathematics behind ML methods

What the module consists of

VorlesungDelivery of theoretical fundamentals, practical examples and code examples; regular homework for application and exam preparation

Teaching method

Vortrag/PräsentationIntroduction and explanation of the theoretical fundamentals
Tablet-PC-DemonstrationenDerivation and illustration of complex concepts
Interaktive Fragen/ DiskussionPromotion of transferability and deeper understanding
Codebeispiele/ProgrammierübungenPractical implementation of ML methods, application to vehicle technology

What you should already know

Grundlagen KFZrecommended
Programmkenntnisse in Python

Assessment

Written examBonus via homework

Written exam (90 minutes). Exam content: fundamentals of machine learning methods, related mathematics and application to vehicle technology problems; allowed aid: calculator. Additionally, there is the possibility to receive a grade bonus according to APSO §6 Abs. 5 for the exam via homework (average ≥ 50%).

Reading list

Bishop - Neural Networks for Pattern Recognition (1995)
Mitchell - Machine Learning (1997)
Bishop - Pattern Recognition and Machine Learning (2007)
Barber - Bayesian Reasoning and Machine Learning (2012)
Nielsen - Neural Networks and Deep Learning (2014)
Pendelten et al. - Perception, Planning, Control, and Coordination for Autonomous Vehicles, Machines 2017

No dates in the current semester
There are no course dates for this module this semester, or they haven't been matched yet.

Show TUMonline data

Sprache: Englisch
Turnus: Wintersemester
Modulniveau: Master
Moduldauer: Einsemestrig
Gesamtstunden: 150
Präsenzstunden: 45
Eigenstudiumstunden: 105
Organisationsname: Department Mobility Systems Engineering

Official page in TUMonline · Details are not binding.