252-0535-00L Advanced Machine Learning
| Semester | Autumn Semester 2018 |
| Lecturers | J. M. Buhmann |
| Periodicity | yearly recurring course |
| Language of instruction | English |
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. |
| 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. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 8 credits |
| Examiners | J. M. Buhmann |
| 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 | 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. |
| 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. | |
Courses
| Number | Title | Hours | Lecturers | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 hrs |
| J. M. Buhmann | ||||||||||||
| 252-0535-00 U | Advanced Machine Learning | 2 hrs |
| J. M. Buhmann | ||||||||||||
| 252-0535-00 A | Advanced Machine Learning Project Work, no fixed presence required. | 2 hrs | J. M. Buhmann |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
