252-0535-00L  Advanced Machine Learning

SemesterHerbstsemester 2020
DozierendeJ. M. Buhmann, C. Cotrini Jimenez
Periodizitätjährlich wiederkehrende Veranstaltung


252-0535-00 VAdvanced Machine Learning
The lectures will mostly be given in a lecture hall with limited attendance (at most 50% of lecture hall capacity). It will be possible to join remotely via zoom with acccess to slides, whiteboard, and speaker camera. Students can interact, e.g. ask questions, physically as well as digitally. The lectures will be recorded via zoom’s recording functionality.
3 Std.
Do15:15-16:00ETA F 5 »
Fr08:15-10:00ETA F 5 »
J. M. Buhmann, C. Cotrini Jimenez
252-0535-00 UAdvanced Machine Learning2 Std.
Mi14:15-16:00CAB G 61 »
16:15-18:00CAB G 61 »
Do16:15-18:00ML F 34 »
Fr14:15-16:00CAB G 61 »
J. M. Buhmann, C. Cotrini Jimenez
252-0535-00 AAdvanced Machine Learning
Project Work, no fixed presence required.
4 Std.J. M. Buhmann, C. Cotrini Jimenez


KurzbeschreibungMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
LernzielStudents will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
InhaltThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

What is data?
Bayesian Learning
Computational learning theory

Supervised learning:
Ensembles: Bagging and Boosting
Max Margin methods
Neural networks

Unsupservised learning:
Dimensionality reduction techniques
Mixture Models
Non-parametric density estimation
Learning Dynamical Systems
SkriptNo lecture notes, but slides will be made available on the course webpage.
LiteraturC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Voraussetzungen / BesonderesThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.

PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.


Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte10 KP
PrüfendeJ. M. Buhmann, C. Cotrini Jimenez
RepetitionDie Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich.
Prüfungsmodusschriftlich 180 Minuten
Zusatzinformation zum PrüfungsmodusThe practical projects are an integral part of the course (60 hours of work, 2 credits). Participation is mandatory. A failing grade for the practical projects will result in a failing grade for the course.

For students who obtain a passing grade for the practical projects, the final grade for the course will be calculated as a weighted average of the grade achieved in the written examination (70%) and the grade achieved in the practical projects (30%).

Students who achieve a failing grade in the practical projects have to de-register from the exam. Otherwise, they will not be admitted to the exam and will be treated as no-shows.

The exam might take place at a computer.
Hilfsmittel schriftlichTwo A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.


Es werden nur die öffentlichen Lernmaterialien aufgeführt.


Keine Informationen zu Gruppen vorhanden.


Keine zusätzlichen Belegungseinschränkungen vorhanden.

Angeboten in

CAS in InformatikFokusfächer und WahlfächerWInformation
Computational Biology and Bioinformatics MasterData ScienceWInformation
Cyber Security MasterKernfächerWInformation
Cyber Security MasterKernfächerWInformation
Cyber Security MasterKernfächerWInformation
Cyber Security MasterWahlfächerWInformation
DAS in Data ScienceMachine Learning and Artificial IntelligenceWInformation
Data Science MasterInformation and LearningWInformation
Doktorat Departement Informationstechnologie und ElektrotechnikLehrangebot Doktorat und PostdoktoratWInformation
Elektrotechnik und Informationstechnologie MasterKernfächerWInformation
Elektrotechnik und Informationstechnologie MasterVertiefungsfächerWInformation
Elektrotechnik und Informationstechnologie MasterEmpfohlene FächerWInformation
Elektrotechnik und Informationstechnologie MasterAdvanced Core CoursesWInformation
Informatik DZFachwissenschaftliche Vertiefung mit pädagogischem FokusWInformation
Informatik LehrdiplomFachwiss. Vertiefung mit pädagogischem Fokus und weitere FachdidaktikWInformation
Informatik MasterKernfächer der Vertiefung in Information SystemsWInformation
Informatik MasterKernfächer der Vertiefung in Computational ScienceWInformation
Informatik MasterKernfächer der Vertiefung in Visual ComputingWInformation
Informatik MasterKernfächer der Vertiefung General StudiesWInformation
Informatik MasterKernfächerWInformation
Informatik MasterKernfächerWInformation
Informatik MasterWahlfächerWInformation
Informatik MasterWahlfächer der Vertiefung in Theoretical Computer ScienceWInformation
Informatik MasterErgänzung in Data ManagementWInformation
Informatik MasterErgänzung in Machine LearningWInformation
Informatik MasterErgänzung in Theoretical Computer ScienceWInformation
Maschineningenieurwissenschaften MasterMechanics, Materials, StructuresWInformation
Neural Systems and Computation MasterWahlfächerWInformation
Quantum Engineering MasterWahlfächerWInformation
Rechnergestützte Wissenschaften BachelorWeitere Wahlfächer aus den Vertiefungsgebieten (RW Master)WInformation
Rechnergestützte Wissenschaften BachelorRobotikWInformation
Rechnergestützte Wissenschaften MasterSystems and ControlWInformation
Rechnergestützte Wissenschaften MasterRobotikWInformation
Robotics, Systems and Control MasterKernfächerWInformation
Statistik MasterStatistische und mathematische FächerWInformation
Statistik MasterFachbezogene WahlfächerWInformation