Credits: 5

Schedule: 13.09.2017 - 08.12.2017

Teaching Period (valid 01.08.2018-31.07.2020): 

I-II (Autumn)
Lectures only in Period I and independent project work in Period II.

Learning Outcomes (valid 01.08.2018-31.07.2020): 

After the course, the student is able to apply the basic machine learning methods to data and to understand new models based on these principles.

Content (valid 01.08.2018-31.07.2020): 

The course deals with basic principles needed to understand and apply machine learning models and methods. The topics include supervised and unsupervised learning, Bayesian decision theory, parametric methods, tuning model complexity, dimensionality reduction, clustering, nonparametric methods, decision trees, comparing and combining algorithms, as well as a few applications of these methods.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

The grading will be based on several multiple choice quizzes, the completion of home assignments, peer reviewing the home assignments of other students and completion of a data analysis project. There will be no written exam.

Workload (valid 01.08.2018-31.07.2020): 

Lectures and exercises.

Study Material (valid 01.08.2018-31.07.2020): 

To be specified in MyCourses at the start of the course.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

Replaces courses T-61.3050 Machine Learning: Basic Principles and T-61.3030 Principles of Neural Computing.

Prerequisites (valid 01.08.2018-31.07.2020): 

CS-C3160 Data Science, CS-C3110 Datasta tietoon or equivalent skills.

Grading Scale (valid 01.08.2018-31.07.2020):