252-0535-00L Advanced Machine Learning
Semester | Herbstsemester 2018 |
Dozierende | J. M. Buhmann |
Periodizität | jährlich wiederkehrende Veranstaltung |
Lehrsprache | Englisch |
Lehrveranstaltungen
Nummer | Titel | Umfang | Dozierende | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
252-0535-00 V | Advanced Machine Learning Vorlesung: Donnerstag im ML D 28 mit Videoübertragung im ML E 12 Freitag im HG F 1 mit Videoübertragung im HG F 3 | 3 Std. |
| J. M. Buhmann | ||||||||||||
252-0535-00 U | Advanced Machine Learning | 2 Std. |
| J. M. Buhmann | ||||||||||||
252-0535-00 A | Advanced Machine Learning Project Work, no fixed presence required. | 2 Std. | J. M. Buhmann |
Katalogdaten
Kurzbeschreibung | Machine 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. |
Lernziel | Students 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. |
Inhalt | The 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: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems |
Skript | No lecture notes, but slides will be made available on the course webpage. |
Literatur | C. 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 / Besonderes | The 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. |
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
ECTS Kreditpunkte | 8 KP |
Prüfende | J. M. Buhmann |
Form | Sessionsprüfung |
Prüfungssprache | Englisch |
Repetition | Die Leistungskontrolle wird nur in der Session nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich. |
Prüfungsmodus | schriftlich 180 Minuten |
Zusatzinformation zum Prüfungsmodus | 70% session examination, 30% project; the final grade will be calculated as weighted average of both these elements. As a compulsory continuous performance assessment task, the project must be passed on its own and has a bonus/penalty function. The practical project are an integral part (60 hours of work, 2 credits) of the course. Participation is mandatory. Failing the project results in a failing grade for the overall examination of Advanced Machine Learning (252-0535-00L). Students who do not pass the project are required to de-register from the exam and will otherwise be treated as a no show. |
Hilfsmittel schriftlich | Two 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. |
Lernmaterialien
Hauptlink | Information |
Es werden nur die öffentlichen Lernmaterialien aufgeführt. |
Gruppen
Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
Keine zusätzlichen Belegungseinschränkungen vorhanden. |