TKO_3120 Machine Learning and Pattern Recognition 5 ECTS
Organised by
Computer Science
Preceding studies
Compulsory:

Learning outcomes

The course gives an overview of many machine learning and pattern recognition methods which can be used to build models and systems based on observed data. After the course students should understand the main principles of machine learning and pattern recognition methods and steps needed for applying them in real applications. The student especially learns the core concepts of overfitting and underfitting and is able to find a suitable balance between these extremes in a given problem at hand.

Contents

This course covers the main theories, techniques, and algorithms in machine learning and pattern recognition, starting with simple topics such as linear regression/classification and ending up with more advanced topics such as artificial neural networks and model complexity selection and performance estimation. For pattern recognition most popular feature extraction techniques are introduced and Bayesian decision theory is studied. Both main unsupervised and supervised learning techniques are considered with emphasize on how, why and when they work.

Teaching methods

Teaching method Contact Online
Lectures 28 h 0 h

Modes of study

Option 1
Available for:
  • Degree Programme Students
  • Other Students
  • Doctoral Students
  • Exchange Students
Written exam
  • In English
Project / practical work
  • In English

Evaluation

Numeric 0-5.

Study materials

Marsland S., Machine Learning: An Algorithmic Perspective,Chapman & Hall/CRC 2009

Theodoridis S. and Koutroumbas K., Pattern Recognition, 4thedition, Elsevier, 2009

Belongs to following study modules

Department of Future Technologies
Department of Future Technologies
Department of Biochemistry
Department of Biochemistry
2016–2017
Teaching
Archived Teaching Schedule. Please refer to current Teaching Shedule.
Department of Future Technologies
DP in Computer Science
DP in Computer Science
DP Bachelor of Science in Techn.(Communication St)
DP in Information and Communication Technology
MDP in Digital Health and Life Sciences (Tech.)
Finnish Study Modules