Applied Machine Intelligence

Applied Machine Intelligence
Lecturer: Klaus Diepold, Christian Keimel
Assistants: Manuel Lengl, Stefan Röhrl
Target Group: Master EI
ECTS: 9
Contact: ami@ldv.ei.tum.de
Turnus: Summer Term
Registration: TUMOnline
Time & Place:

every Tuesday 09:45 - 13:00 | Room 0670ZG

Begin: first lecture: 26.04.2022

Target Group and Registration

Prerequisites:

Successful completion of at least one of the following lectures:

  • EI70360 Machine Learning and Optimization
  • EI70380 Signal Processing and Machine Learning
  • EI70130 Machine Learning in Robotics
  • EI70150 Pattern Recognition

Furthermore the lecture assumes basic knowledge of general topics discussed at undergraduate level (BSc.) in one of the following areas:

  • Linear Algebra
  • Computer engineering
  • Communications engineering
  • Multimedia technology and human machine interaction

Additionally, basic knowledge of Python (or the motivation to learn it) is recommended.

Content

  • Lifecycle of a Data Analysis or Machine Learning task
  • Data Preprocessing
  • Regression (Algorithms and Metrics)
  • Classification (Algorithms and Metrics)
  • Deep Learning
  • Model Selection
  • Validation Techniques
  • Model Interpretation