252-0535-00L Advanced Machine Learning
|Semester||Autumn Semester 2020|
|Lecturers||J. M. Buhmann, C. Cotrini Jimenez|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|252-0535-00 V||Advanced 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.
|J. M. Buhmann, C. Cotrini Jimenez|
|252-0535-00 U||Advanced Machine Learning||2 hrs|
|J. M. Buhmann, C. Cotrini Jimenez|
|252-0535-00 A||Advanced Machine Learning|
Project Work, no fixed presence required.
|4 hrs||J. M. Buhmann, C. Cotrini Jimenez|
|Abstract||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.|
|Objective||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.|
|Content||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:
What is data?
Computational learning theory
Ensembles: Bagging and Boosting
Max Margin methods
Dimensionality reduction techniques
Non-parametric density estimation
Learning Dynamical Systems
|Lecture notes||No lecture notes, but slides will be made available on the course webpage.|
|Literature||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.
|Prerequisites / Notice||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.
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.
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||10 credits|
|Examiners||J. M. Buhmann, C. Cotrini Jimenez|
|Language of examination||English|
|Repetition||The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.|
|Mode of examination||written 180 minutes|
|Additional information on mode of examination||The 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.
|Written aids||Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.|
|This information can be updated until the beginning of the semester; information on the examination timetable is binding.|
|Only public learning materials are listed.|
|No information on groups available.|
|There are no additional restrictions for the registration.|