- Bayes classifiers.
- Logistic regression.
- Perceptron.
- Support vector machines.
- Clustering.
- Factor analysis.
- Neural networks architecture.
- Forward- and backpropagation.
- Markov decision processes.
- Bellman equations.
- Deep Q-learning.
Module MV-MEC-M193-M-7
Machine Learning (M, 5.0 LP)
Module Identification
Module Number | Module Name | CP (Effort) |
---|---|---|
MV-MEC-M193-M-7 | Machine Learning | 5.0 CP (150 h) |
Basedata
CP, Effort | 5.0 CP = 150 h |
---|---|
Position of the semester | 1 Sem. in SuSe |
Level | [7] Master (Advanced) |
Language | [EN] English |
Module Manager | |
Lecturers | |
Area of study | [MV-MEC] Mechatronics in Mechanical and Automotive Engineering |
Reference course of study | [MV-88.808-SG] M.Sc. Computational Engineering |
Livecycle-State | [NORM] Active |
Courses
Type/SWS | Course Number | Title | Choice in Module-Part | Presence-Time / Self-Study | SL | SL is required for exa. | PL | CP | Sem. | |
---|---|---|---|---|---|---|---|---|---|---|
2V+1U | MV-MEC-86692-K-7 | Machine Learning
| P | 42 h | 108 h | - | - | PL1 | 5.0 | SuSe |
- About [MV-MEC-86692-K-7]: Title: "Machine Learning"; Presence-Time: 42 h; Self-Study: 108 h
Examination achievement PL1
- Form of examination: oral examination (30-45 Min.)
- Examination Frequency: each semester
- Examination number: 10692 ("Machine Learning")
Evaluation of grades
The grade of the module examination is also the module grade.
Contents
Competencies / intended learning achievements
1. Lecture:
Machine learning plays an important role in a variety of applications such as data mining, natural language processing, image recognition, expert systems, and autonomous driving. The lecture provides an overview of the basic concepts, techniques, and algorithms of modern machine learning. It also aims to provide rigorous mathematical foundations of the methods covered.
2. Exercise:
Students will get hands-on experience in applying machine learning algorithms on real life problems.
Literature
- Murphy, K.: “Machine Learning: A probabilistic perspective”. Adaptive Computation and Machine Learning Series. The MIT Press, 2012.
- Sutton, R.S., Barto, A.G.: “Reinforcement Learning: An introduction”. Cambridge: MIT Press, 1998.
- Goodfellow I., Bengio Y., Courville A.: “Deep Learning”. Cambridge: MIT Press, 2016.
- Hastie, T., Tibshirani, R., Friedman, J.:“The Elements of Statistical Learning: Data Mining, Inference, and Prediction”. Springer Series in Statistics, Springer, 2016.
Requirements for attendance (informal)
Lectures on higher mathematics
Requirements for attendance (formal)
None
References to Module / Module Number [MV-MEC-M193-M-7]
Module-Pool | Name | |
---|---|---|
[MV-ALL-MPOOL-6] | Wahlpflichtmodule allgemein | |
[MV-CE-MPOOL-6] | Wahlpflichtmodule Computational Engineering | |
[MV-MBINFO-MPOOL-6] | Wahlpflichtmodule Maschinenbau mit angewandter Informatik |
Notes on the module handbook of the department Mechanical and Process Engineering
Ausnahmen: