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COURSE DESCRIPTION AND APPLICATION INFORMATION

Course Name Code Semester T+A+L (hour/week) Type (C / O) Local Credit ECTS
Machine Learning CE 609 Fall-Spring 03+00+00 Elective 3 7.5
Academic Unit: Computer Engineering
Mode of Delivery: Face to face
Prerequisites: None
Language of Instruction: English
Level of Course Unit: Doctorate
Course Coordinator: Habib ŞENOL
Course Objectives: This course will provide students an overview of issues, algorithms and techniques in machine learning. Students will also gain theoretical and practical experience through programming exercises and projects.
Course Contents: Introduction, review of probability, statistics and linear algebra; Supervised Learning; Bayesian Decision Theory; Parametric Methods ; Multivariate Methods; Dimensionality Reduction; Clustering; Nonparametric Methods; Decision Trees; Linear Dicrimination Support Vector Machines; Multilayer Perceptrons; Hidden Markov Models; Assessing and Comparing Classification Algorithms;Combining Multiple Learners
Learning Outcomes of the Course Unit (LO):
    Planned Learning Activities and Teaching Methods: Lecture


    WEEKLY SUBJECTS AND RELATED PREPARATIONS

    WeekSubjectsRelated Preperation


    REQUIRED AND RECOMMENDED READING

    Alpaydin, E., 2004. Introduction to Machine Learning (Adaptive Computation
    and Machine Learning), The MIT Press.


    OTHER COURSE RESOURCES

    Bishop, C., 2006. Pattern Recognition and Machine Learning, Springer.


    ASSESSMENT METHODS AND CRITERIA

    Semester RequirementsNumberPercentage of Grade (%)
    Total: 0 0


    WORKLOAD

    EventsCountDuration (Hours)Total Workload (hour)
    Total Workload (hour):0


    THE RELATIONSHIP BETWEEN COURSE LEARNING OUTCOMES (LO) AND PROGRAM QUALIFICATIONS (PQ)

    # PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10