Margin-based models and kernels. Classification and Support vector machines. Ranking and preference learning. Unsupervised learning with kernels. Kernels for structured data. Multilabel classification. Semi-supervised learning. Predicting structured output. Convex optimization methods.
Course position and Prerequisites
Course is advanced MSc course in Machine learning, targeted to 2nd year MSc students in Machine Learning, Bioinformation technology and Computer science. Also suitable for PhD studies.
- The course Machine Learning: Basic principles (or equivalent knowledge), strongly recommended
- MATLAB programming skills are recommended (course material will use matlab), you can use R or Python (but no guidance will be available from teachers)
After attending the course, the student knows how kernel methods can be used in various machine learning tasks, including classification, ranking and preference learning, as well as learning with multiple data sources and targets. The student knows how convex optimization methods can be used to efficiently train kernel-based models. The student knows how structured data such as sequences, hierarchies and graphs can be tackled through kernel methods.
- Lectures: Wednesdays 12:15-14:00, lecture hall T2
- Tutorial sessions: Fridays 8:30-10, Otakaari 1, lecture hall U154 (U1), alternating between Q&A sessions (help for solving the exercises) and solution sessions (presenting the solutions for the exercise set). Attending the sessions is voluntary.
- Exam: 18.12.2017, 9-12 lecture hall T1
- Lecturer: Prof. Juho Rousu
- Course assistants: Dr Sandor Szedmak, Dr Celine Brouard, Parisa Mapar
The course can be completed by two alternative ways:
- Exercises (max. 60 points + Bonus points) + Exam (max. 40 points), giving a grade 0..5. Lowest passing points total is 50. 85 points will give the grade of 5.
- Exam only (max. 40 points), giving a grade 0...5. 20 points will give the grade 1, 34 points will give the grade of 5.
The better of the resulting two grades will be taken into account.
Language of Instruction
- Lecture slides and exercises are the examined content
- Course book (will be loosely followed): Shawe-Taylor and Cristianini: Kernel Methods for Pattern Analysis, Cambridge University Press, 2004. Available as ebook: http://site.ebrary.com/lib/aalto/detail.action?docID=10131674
- N. Cristianini and J. Shawe-Taylor: Introduction to Support Vector Machines and other kernel-based learning methods. http://books.google.fi/books?isbn=0521780195
- B. Scholkopf, A. Smola: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. http://books.google.fi/books?isbn=0262194759
- Research papers provided during the course (See Materials).