First Cycle - Faculty of Engineering - Computer Engineering (English)
Y : Year of Study S : Semester
Course Unit Code Course Unit Title Type of Course Y S ECTS
CSE4088 Inroduction to Machine Learning Compulsory 4 7 5
Objectives of the Course
This introductory course on machine learning will give an overview of many techniques and algorithms in machine learning, beginning with topics such as supervised learning, Bayesian networks and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, reinforcement learning. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.
Learning Outcomes
1 utilize and construct HMMs as system models
2 understand the linear discrimination and multilayer perceptrons
3 use decision trees to solve related problems
4 discuss the circumstances at which nonparametric methods should be used rather than parametric ones and apply them
5 analyze and note the performance difference among clustering mechanisms and correctly utilize them
6 understand and apply the best fitting among the dimensionality reduction methods
7 use parametric uni- and multi-variate methods to estimate the distribution of sample data
8 apply supervised learning to classification problems
Mode of Delivery
Formal Education
Recommended Optional Programme Components
None
Course Contents
This introductory course on machine learning will give an overview of many techniques and algorithms in machine learning, beginning with topics such as supervised learning, Bayesian networks and ending up with more recent topics such as boosting, support vector machines, hidden Markov models, reinforcement learning. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how and why they work. The underlying theme in the course is statistical inference as this provides the foundation for most of the methods covered.
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1 Introduction (What is ML? Example Applications)
2 Supervised Learning
3 Bayesian Decision Theory
4 Parametric Methods
5 Multivariate Methods
6 Dimensionality Reduction
7 Clustering
8 Study Week
9 Midterm Exam
10 Nonparametric Methods
11 Decision Trees
12 Linear Discrimination
13 Multilayer Peceptrons
14 Hidden Markov Models (HMMs)
15 Reinforcement Learning
16 Final Exam Study
17 Final Exam
Recommended or Required Reading
Introduction to Machine Learning, second edition, Ethem ALPAYDIN, The MIT Press, 2010
Planned Learning Activities and Teaching Methods
Lecture notes, slides, handouts, examples of programming
Assessment
AssessmentQuantityWeight
Term (or Year) Learning Activities60
End Of Term (or Year) Learning Activities40
Total100
Term (or Year) Learning ActivitiesQuantityWeight
Midterm Exam150
Project450
Total100
End Of Term (or Year) Learning ActivitiesQuantityWeight
Final Exam1100
Total100
Language of Instruction
Language Codes
Work Placement(s)
None
Workload Calculation
Activities Number Time (hours) Total Work Load (hours)
Theoretical 3 14 42
Midterm Preparation 1 10 10
Final Preparation 1 13 13
Project 3 20 60
Total 8 57 125
Contribution of Learning Outcomes to Programme Outcomes
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