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Advanced Signal Processing & Communications Engineering (Master of Science) >>

Lab Course Machine Learning in Signal Processing (LabMLISP)2.5 ECTS
(englische Bezeichnung: Lab Course Machine Learning in Signal Processing)
(Prüfungsordnungsmodul: Praktikum Machine Learning in der Signalverarbeitung)

Modulverantwortliche/r: André Kaup
Lehrende: Kamal Gopikrishnan Nambiar

Startsemester: SS 2021Dauer: 1 SemesterTurnus: halbjährlich (WS+SS)
Präsenzzeit: 60 Std.Eigenstudium: 15 Std.Sprache: Englisch


Empfohlene Voraussetzungen:

Knowledge of Python programming language is required. Basic theoretical knowledge in machine learning is assumed: consider taking the Machine Learning in Signal Processing (MLSIP) course in the same semester.


This is an advanced level lab course in machine learning. Imagine a car driving on an autobahn in an automatic mode. Among other things, the car needs to steer itself to keep driving in it's own lane. To accomplish this, the central problem is to detect the road-lane markings. These are the white solid or dashed lines that are drawn on each side of the lane. The standard modern approach to solve this type of problems is to take a large dataset of labeled examples and train a deep neural network model to accomplish the task. This is how car and pedestrian detection algorithms are developed. The difficulty with the road-lane markings is that there is no labeled dataset of them and creating such dataset would cost millions of dollars.

In this lab course we will solve this problem using transfer learning and mathematical modeling:

  • Create cartoon-like artificial images of a road with known locations for the lane markings.

  • Train deep neural network on these artificial images with heavy data augmentations that mimic real-world images.

  • Create a dataset of unlabeled real-life videos by downloading and organizing examples from youtube.

  • Create a machine learning pipeline for working with these videos efficiently.

  • Apply the neural network that has been trained on artificial data to the real world videos.

  • Analyze the quality of results produced by the network.

  • Use mathematical modeling to correct the outputs of the network.

  • Retrain the network on the dataset composed of the corrected outputs.

  • Measure and analyze the quality of the results.

The software will be written in Python using JupyterLab development framework. Access to modern GPU server will be provided. The best students will have the opportunity to contribute to the creation of state-of-the-art lane detection system for self-driving cars during or after the corse.

Lernziele und Kompetenzen:

Students are able to:

  • Independently design machine learning pipelines to solve complex problems in artificial intelligence.

  • Choose appropriate algorithms for the problem at hand.

  • Use standard packages for machine learning in Python: numpy, cvxpy, scikit-learn, pywavelets, pytorch.

  • Debug and calibrate machine learning algorithms. Develop modification to the standard algorithms as appropriate to the problem at hand.

  • Explain the theoretical aspects of deep learning.

Weitere Informationen:

www: https://lms.tf.fau.de/studium-und-lehre/lehrveranstaltungen/

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

  1. Advanced Signal Processing & Communications Engineering (Master of Science)
    (Po-Vers. 2020w | TechFak | Advanced Signal Processing & Communications Engineering (Master of Science) | Gesamtkonto | Technical Lab Courses | Praktikum Machine Learning in der Signalverarbeitung)
Dieses Modul ist daneben auch in den Studienfächern "Communications and Multimedia Engineering (Master of Science)", "Information and Communication Technology (Master of Science)", "Informations- und Kommunikationstechnik (Master of Science)", "Mechatronik (Master of Science)" verwendbar. Details


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