Advanced Course in Machine Learning
Exam
Year | Semester | Date | Period | Language | In charge |
---|---|---|---|---|---|
2017 | spring | 14.03-05.05. | 4-4 | English | Arto Klami |
Lectures
Time | Room | Lecturer | Date |
---|---|---|---|
Tue 10-12 | D122 | Arto Klami | 14.03.2017-11.04.2017 |
Thu 12-14 | D122 | Arto Klami | 16.03.2017-06.04.2017 |
Thu 12-14 | D122 | Arto Klami | 20.04.2017-04.05.2017 |
Tue 10-12 | D122 | Arto Klami | 25.04.2017-02.05.2017 |
Exercise groups
Time | Room | Instructor | Date | Observe |
---|---|---|---|---|
Fri 12-14 | B221 | Aditya Jitta | 17.03.2017—07.04.2017 | |
Fri 12-14 | B221 | Aditya Jitta | 21.04.2017—05.05.2017 |
Ilmoittautuminen tälle kurssille alkaa tiistaina 16.2. klo 9.00.
Registration for this course starts on Tuesday 16th of February at 9.00.
General
The course has a Moodle page that contains all the material (lecture slides and exercise problems). It is also used for turning in the exercise problem solutions.
The course is a natural contiuation for the Introduction to machine learning -course. It covers more topics and also goes deeper, discussing both theory and practice of machine learning.
The topics include (slight changes are to be expected):
- Machine learning in general; what it is about, what can it achieve, and what are the underlying fundamentals
- Probabilitistic perspective to machine learning, some Bayesian ifnerence
- Optimization and regularization
-
Unsupervised learning
- Clustering, mixture models
- Linear latent variable models (PCA, ICA, etc)
- Non-linear dimensionality reduction
- Matrix factorization, recommender engines
-
Supervised learning
- Regression and classification; basic principles
- Kernel methods and support vector machines
- Decision trees, ensembles and boosting
- Neural networks and deep learning
Completing the course
There are two alternative ways to complete the course:
- Solve sufficient proportion of the weekly exercises and attend the course exam (or a later separate exam)
- Solve a small research project and attend a separate exam. The project details are available in Moodle, but can be downloaded also directly from here
The exercise session is not obligatory. Instead, it is simply a session during which the course organizers will be available to help with the exercises.
Literature and material
The course will be primarily lectured based on the book "Machine learning: A probabilistic perspective" by Kevin P. Murphy, but most of the material can also be found in freely available sources. Links to alternative readinig sources are provided in the Lectures tab (and sometimes in the exercise problems).