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Course module: 201600070
201600070
Machine Learning I
Course info
Course module201600070
Credits (ECTS)5
Course typeCourse
Language of instructionEnglish
Contact persondr.ing. G. Englebienne
E-mailg.englebienne@utwente.nl
Lecturer(s)
PreviousNext 2
Examiner
dr.ing. G. Englebienne
Contactperson for the course
dr.ing. G. Englebienne
Lecturer
dr.ing. G. Englebienne
Lecturer
dr. E. Mocanu
Lecturer
dr.ir. D.C. Mocanu
Academic year2021
Starting block
1A
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes
Aims
After completing this course successfully, the student can:
  • Describe and compare basic machine learning methods and techniques, associated complexity and the application domain. Theories and techniques include: Bayesian classification theory, Decision Trees, Linear Discrimination, Neural Networks, Support Vector Machines.
  • Design, implement and systematically evaluate machine learning methods and models.
Content
The course is an in-depth introduction to the theory and practicalities of Machine Learning (ML), in which the emphasis is on an overview of the various techniques, their workings, associated complexity and application domains. We  also look into the theoretical aspects of machine learning techniques, such as over- and under-fitting and the Bias/Variance Dilemma.  Emphasis is on basic ML models, on methodology (how to achieve reliable models systematically) and the evaluation of the learnt/trained models. 
 
Prerequisites
  • Working knowledge in probability theory and statistics. This is covered in the course Probability Theory, or equivalent.
  • Basic working knowledge in linear algebra
  • Knowledge about basic AI formalisms for defining and solving problems: search, representation of knowledge, reasoning, learning and reasoning under uncertainty. This is covered in the bachelors course Artificial Intelligence (202000993).
 
Content keywords
Machine Learning, Supervised Learning, Bayesian Decision Theory and Models, Parametric Models, Decision Trees, Linear Discrimination, Neural Networks, Support Vector Machines, Evaluation of Classifiers.
 
Participating study
Master Interaction Technology
Participating study
Master Computer Science
Required materials
Book
Christopher M. Bishop: Pattern Recognition and Machine Learning, Springer 2006. ISBN-13: 978-0387-31073-2
Recommended materials
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Instructional modes
Assignment

Lecture

Practical
Presence dutyYes

Self study without assistance

Tests
Assignments

Remark
Homework Assignments, Practical Assignments and Written exam

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Kies de Nederlandse taal