252-0535-00L Advanced Machine Learning
| Semester | Autumn Semester 2022 |
| Lecturers | J. M. Buhmann, C. Cotrini Jimenez |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 252-0535-00 V | Advanced Machine Learning Freitag 8-10 HG F1 mit Videoübertragung ins HG F3 | 3 hrs |
| J. M. Buhmann, C. Cotrini Jimenez | |||||||||
| 252-0535-00 U | Advanced Machine Learning Tutorials and QA sessions: Please attend only the tutorial assigned to you by the first letter of your surname. In case of collisions, please attend via Zoom the last tutorial of the week or watch its recording later. We do not attend requests to change tutorials. QA sessions will happen fully remotely with no recording happening. Wed 14-16 - CAB 61 - surname A-L Fri 14-16 - CAB G1 - surname M-Z (offered also via Zoom to anyone) Thu 17-18 (QA session) - Fully virtual through Zoom All tutorial sessions are identical. | 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 |
Catalogue data
| 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. |
| Learning 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: 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 |
| 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
| 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 |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling. |
| 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. | |
Learning materials
| Main link | Information |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
