700.610 (23S) Fundamentals of Machine Learning with application to Energy Informatics and Networked Systems
Überblick
- Lehrende/r
- LV-Titel englisch Fundamentals of Machine Learning with application to Energy Informatics and Networked Systems
- LV-Art Kurs (prüfungsimmanente LV )
- LV-Modell Onlinelehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 3.0
- Anmeldungen 5 (30 max.)
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 28.04.2023
- eLearning zum Moodle-Kurs
Zeit und Ort
LV-Beschreibung
Intendierte Lernergebnisse
Methods & Goals
The aim of FOML is to communicate the fundamental principles and classical
techniques of Machine Learning to students, wich is essential to understand
modern Deep Learning techniques.
Besides theoretical input in form of slides and tutorials, students will work on
basic Machine Learning problems i.e. produce code in Python. Beyond creating
and training the model, students will learn how to conduct a ML study from
beginning to end, from problem definition to containerization using Docker.
In view of tools, this course will make use of open-source software: Python,
Scikit-Learn, Keras and possibly also PyTorch.
Lehrmethodik inkl. Einsatz von eLearning-Tools
Methods & Goals
The aim of FOML is to communicate the fundamental principles and classical
techniques of Machine Learning to students, wich is essential to understand
modern Deep Learning techniques.
Besides theoretical input in form of slides and tutorials, students will work on
basic Machine Learning problems i.e. produce code in Python. Beyond creating
and training the model, students will learn how to conduct a ML study from
beginning to end, from problem definition to containerization using Docker.
In view of tools, this course will make use of open-source software: Python,
Scikit-Learn, Keras and possibly also PyTorch.
Inhalt/e
This course on fundamentals of machine learning - FOML - intends to
communicate the basics of classical Machine Learning theory to Math, IT, and
ICT students. Building on basic Math skills students had gained in high school,
FOML introduces students to the broad field, teaches basic principles and
classical ML techniques (SVM, Random Forest, etc.), which is essential to
understand state-of-the-art Deep Learning techniques. As showcases, this
course will deal with everyday Machine Learning problems of the Energy
Informatics research field such as forecasting of time series, detecting
anomalies in energy grids and clustering of consumers based on household
appliances.
It is intended to have FOML laid out as conventional "Kurs" with 2 SWS and
3ECTS, starting in Sommersemester 2023. Though the case studies will focus
on energy data, this course welcomes students of any technical background.
Modules
1. Introduction to Machine Learning
i. Basic Terms and Definitions
ii. Supervised vs. Unsupervised
iii. Bagging vs Boosting
iv. Overfitting vs Underfitting
2. Module on Regression and Classification
i. Linear Regression / Least Squares
ii. Support Vector Regression
iii. Naive Bayes
iv. k-Nearest Neighbours
v. Decision Tree
vi. Ensemble Methods
vii. Random Forest
3. Module on Clustering
i. Intro to unsupervised learning
ii. k-means
iii. Hierarchical clustering
iv. DBSCAN
4. Module on Features
i. Dimensionality Reduction
ii. Feature Engineering
iii. Model Selection
iv. Parameter tuning
v. Pre/Postprocessing
5. Module on Modern Technical Aspects
i. Containerization using Docker
ii. Deploying ML models on the cloud
iii. CI / CD life cycle
6. Outlook on modern Machine Learning
i. Intro to Neural Networks
ii. Brief Review of Deep Learning (LSTM, GRU, Transformer)
iii. Review of recent breakthroughs
Prüfungsinformationen
Beurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Besonderer Studienbereich Nachhaltigkeit
(SKZ: 999, Version: 12W.1)
-
Fach: LV-Pool
(Wahlfach)
-
Nachhaltigkeit (
4.0h SE / 8.0 ECTS)
- 700.610 Fundamentals of Machine Learning with application to Energy Informatics and Networked Systems (2.0h KS / 3.0 ECTS)
-
Nachhaltigkeit (
4.0h SE / 8.0 ECTS)
-
Fach: LV-Pool
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.610 Fundamentals of Machine Learning with application to Energy Informatics and Networked Systems (2.0h KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 15W.1)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
- 700.610 Fundamentals of Machine Learning with application to Energy Informatics and Networked Systems (2.0h KS / 3.0 ECTS)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Fach: Information and Communications Engineering: Supplements (NC, ASR)
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
- 700.610 Fundamentals of Machine Learning with application to Energy Informatics and Networked Systems (2.0h KS / 3.0 ECTS)
-
1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: Information and Communicatons Enginnering: Supplements
(Wahlfach)
- Masterstudium Information and Communications Engineering (ICE)
(SKZ: 488, Version: 22W.1)
-
Fach: ICE- Supplements
(Wahlfach)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
- 700.610 Fundamentals of Machine Learning with application to Energy Informatics and Networked Systems (2.0h KS / 3.0 ECTS)
-
2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) (
0.0h VC, KS / 14.0 ECTS)
-
Fach: ICE- Supplements
(Wahlfach)
- Erweiterungsstudium (UG §54a) Nachhaltige Entwicklung und Energie (NhEE) (ES)
(SKZ: 011, Version: 20W.1)
-
Fach: Energiemanagement und -technik
(Pflichtfach)
-
2.1 Smart Grids (
0.0h VC / 4.0 ECTS)
- 700.610 Fundamentals of Machine Learning with application to Energy Informatics and Networked Systems (2.0h KS / 3.0 ECTS)
-
2.1 Smart Grids (
0.0h VC / 4.0 ECTS)
-
Fach: Energiemanagement und -technik
(Pflichtfach)
- Erweiterungsstudium (UG §54a) Nachhaltige Entwicklung und Energie (NhEE) (ES)
(SKZ: 011, Version: 20W.1)
-
Fach: Gebundenes Wahlfach 2: Vertiefung zu Energiemanagement und -technik
(Wahlfach)
-
4.1 Lehrveranstaltungen aus Vertiefung zu Energiemanagement und -technik (
0.0h XX / 4.0 ECTS)
- 700.610 Fundamentals of Machine Learning with application to Energy Informatics and Networked Systems (2.0h KS / 3.0 ECTS)
-
4.1 Lehrveranstaltungen aus Vertiefung zu Energiemanagement und -technik (
0.0h XX / 4.0 ECTS)
-
Fach: Gebundenes Wahlfach 2: Vertiefung zu Energiemanagement und -technik
(Wahlfach)
- Erweiterungsstudium (UG §54a) Nachhaltige Entwicklung und Energie (NhEE) (ES)
(SKZ: 011, Version: 20W.1)
-
Fach: Gebundenes Wahlfach 3: Spezialisierung in einem der Vertiefungsfächer
(Wahlfach)
-
5.1 Lehrveranstaltungen Spezialisierung in einem der Vertiefungsfächer (
0.0h XX / 4.0 ECTS)
- 700.610 Fundamentals of Machine Learning with application to Energy Informatics and Networked Systems (2.0h KS / 3.0 ECTS)
-
5.1 Lehrveranstaltungen Spezialisierung in einem der Vertiefungsfächer (
0.0h XX / 4.0 ECTS)
-
Fach: Gebundenes Wahlfach 3: Spezialisierung in einem der Vertiefungsfächer
(Wahlfach)