Machine Learning for Engineers I: Introduction to Methods and Tools (MLE1)5 ECTS
(englische Bezeichnung: Machine Learning for Engineers I: Introduction to Methods and Tools)
(Prüfungsordnungsmodul: Machine Learning for Engineers - Introduction to Methods and Tools)

Lectures:

• • Machine Learning for Engineers I: Introduction to Methods and Tools
    (Vorlesung, Björn Eskofier et al., See VHB for further details (https://kurse.vhb.org/))

Inhalt:

This course offers an overview of some of the most widely used machine learning (ML) methods that are required for solving data science problems. We present the necessary fundamental for each topic and provide programming exercises.
The course includes:
1) The common practices for data preGprocessing.
2) Teaching different tasks regarding regression, classification, and dimensionality reduction using methods including but not limited to linear regression and classification, Support vector machines and Deep neural networks.
3) Introduction to Python programming for data science.
4) Applying machine learning models on real world engineering applications.

Lernziele und Kompetenzen:

• Understanding the fundamental of data science and machine learning domain

• Understanding some of the most widely used machine learning methods

• Being able to implement machine learning pipeline in order to solve real world problems

Literatur:

Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT press,2012
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer, 2009
Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016

Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:

1. Data Science (Bachelor of Science)
   (Po-Vers. 2022s | Gesamtkonto | Kernmodule Data Science | Wahlpflichtmodul Machine Learning | Machine Learning for Engineers - Introduction to Methods and Tools)
2. Data Science (Bachelor of Science)
   (Po-Vers. 2022s | Gesamtkonto | Vertiefungsrichtungen | Maschinelles Lernen / Artificial Intelligence (AI) | Machine Learning for Engineers - Introduction to Methods and Tools)
3. Data Science (Bachelor of Science)
   (Po-Vers. 2022s | Gesamtkonto | Vertiefungsrichtungen | Nicht gewählte Vertiefungsrichtungen | Machine Learning for Engineers - Introduction to Methods and Tools)

Studien-/Prüfungsleistungen:

Machine Learning for Engineers - Introduction to Methods and Tools (Prüfungsnummer: 50671)

Prüfungsleistung, elektronische Prüfung, Dauer (in Minuten): 90, benotet, 5 ECTS

Anteil an der Berechnung der Modulnote: 100.0 %

weitere Erläuterungen:
The questions are based on both understanding the methods and ability to implement them. A project work should be successfully completed.
Digital Open Book exam via StudOn

Prüfungssprache: Englisch

Erstablegung: WS 2022/2023, 1. Wdh.: SS 2023

1. Prüfer:

Björn Eskofier

Termin: 06.08.2022, 10:00 Uhr, Ort: StudOn Exam