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)
- 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, room T3
- Tutorial sessions: Fridays 8-10, room T3, alternating between guidance sessions (help for solving the exercises) and solution sessions (presenting the solutions for the exercise set).
- Exam: 19.12.2016, 9-12 lecture hall T1
- Lecturer: Prof. Juho Rousu
- Course assistants: Huibin Shen, Dr Celine Broard, Dr Sahely Bhadra
Grading is based on points from the exercises (60 available + bonus points) and the exam (60 available). Maximum of the exercise and exam points is taken for determining the grade for the course.
Grading scale 0-5 will be used. 50% of total gives the grade 1/5, 85% of total gives the grade 5/5.
Grade boundaries: 30-35: 1, 36-40: 2, 41-45: 3, 46-50: 4, 51- : 5
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
- Research papers provided during the course.
- 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