INTERNATIONAL BURCH UNIVERSITY
Graduate Study - Faculty of Engineering and Natural Sciences
3+2 Information Technology Master
2011-2012

SYLLABUS
Code Name Level Year Semester
CEN 559 Machine Learning Graduate 1 Fall
Status Number of ECTS Credits Class Hours Per Week Total Hours Per Semester Language
Area Elective 7.5 3 1 English

Instructor Assistant Coordinator
Abdülhamit Subaşı, Prof. Dr. Abdülhamit Subaşı, Prof. Dr.
[email protected] no email

COURSE OBJECTIVE
Present the key algorithms and theory that form the core of machine learning.
Draw on concepts and results from many fields, including statistics, artifical intelligence, philosophy, information theory, biology, cognitive science, computational complexity, and control theory.

COURSE CONTENT
Week
Topic
  1. Concept of Learning
  2. Bayesian Learning,
  3. Computational Learning Theory
  4. Machine learning techniques and statistical pattern recognition
  5. supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks)
  6. supervised learning (support vector machines)
  7. unsupervised learning (clustering, dimensionality reduction, kernel methods)
  8. learning theory (bias/variance tradeoffs; VC theory; large margins)
  9. reinforcement learning and adaptive control
  10. applications areas (robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing).
  11. Evaluation Hypotheses
  12. Decision Tree Learning
  13. Presentation
  14. Presentation

LABORATORY/PRACTICE PLAN
Week
Topic

    TEACHING/ASSESSMENT
    Description
    • Lectures
    • Presentation
    Description (%)
    Method Quantity Percentage (%)
    Project125
    Midterm Exam(s)125
    Final Exam150
    Total: 100
    Learning outcomes
    • Demonstrate a systematic and critical understanding of the theories, principles and practices of computing;
    • Creatively apply contemporary theories, processes and tools in the development and evaluation of solutions to problems in machine learning;
    • Actively participate in, reflect upon, and take responsibility for, personal learning and development, within a framework of lifelong learning and continued professional development
    • Present issues and solutions in appropriate form to communicate effectively with peers and clients from specialist and non-specialist backgrounds;
    • Work with minimum supervision, both individually and as a part of a team, demonstrating the interpersonal, organisation and problem-solving skills supported by related attitudes necessary to undertake employment.
    TEXTBOOK(S)
    • Du and Swamy, Neural Networks in a Softcomputing Framework, Springer-Verlag London Limited, 2006.

    ECTS (Allocated based on student) WORKLOAD
    Activities Quantity Duration (Hour) Total Work Load
    Lecture (14 weeks x Lecture hours per week)111
    Laboratory / Practice (14 weeks x Laboratory/Practice hours per week) 0
    Midterm Examination (1 week) 0
    Final Examination(1 week) 0
    Preparation for Midterm Examination 0
    Preparation for Final Examination 0
    Total Workload: 1
    ECTS Credit (Total workload/25): 0