INTERNATIONAL BURCH UNIVERSITY
Graduate Study - Faculty of Engineering and Natural Sciences
3+2 Information Technology Master
2016-2017

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 6 3 107 English

Instructor Assistant Coordinator
Zeynep Orhan, Assist. Prof. Dr. Asst. Prof. Dr. Zeynep Orhan Zeynep Orhan, Assist. Prof. Dr.
[email protected] [email protected] no email

This course includes the primary algorithms and approaches to machine learning, theoretical results on the feasibility of various learning tasks and the capabilities of specific algorithms, and examples of practical applications of machine learning to real-world programs. The chapters include instance-based learning methods, genetic algorithms and genetic programming, learning sets of rules, explanation based learning, analytical learning, combining inductive and analytical learning, and reinforcement learning

COURSE OBJECTIVE
Ders hedefleri: This course includes the primary algorithms and approaches to machine learning, theoretical results on the feasibility of various learning tasks and the capabilities of specific algorithms, and examples of practical applications of machine learning to real-world programs. The chapters include classification, clustering, feature selection, evaluation metrics, case-studies etc. At the end of the course students will -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core.

COURSE CONTENT
Week
Topic
  1. Introduction / Orientation
  2. Introduction / Orientation
  3. Classification
  4. Classification
  5. Clustering
  6. Clustering
  7. Midterm Exam
  8. Midterm Exam
  9. Feature selection
  10. Evaluation Metrics
  11. Evaluation Metrics
  12. Case studies
  13. Presentations
  14. Presentations
  15. Workshop

LABORATORY/PRACTICE PLAN
Week
Topic

    TEACHING/ASSESSMENT
    Description
    • Interactive Lectures
    • Practical Sessions
    • Presentation
    • Discussions and group work
    • Assignments
    • Case Studies
    • Other:Term paper, project, workshop
    Description (%)
    Method Quantity Percentage (%)
    Homework15
    Midterm Exam(s)115
    Presentation115
    Term Paper110
    Final Exam130
    Total: 75
    Learning outcomes
    • Define the main concepts in data mining; understanding of potential applications of data mining techniques
    • Review basic concepts in probability and information theory and numerical prediction with linear regression methods
    • Examine algorithms for decision trees; learning bias; dealing with missing values; data overfitting and pruning; learning with numeric attributes and classes
    • Characterize lazy learning approaches; similarity between instances; classification with probabilities
    • Express clustering and classification; the algorithmic details of K-Means and its extensions
    • Discover the relationship between HMM and Bayes theorem and discover the forward algorithm
    • Discover the motivation and use of association analysis
    TEXTBOOK(S)

      ECTS (Allocated based on student) WORKLOAD
      Activities Quantity Duration (Hour) Total Work Load
      Lecture (14 weeks x Lecture hours per week)14342
      Laboratory / Practice (14 weeks x Laboratory/Practice hours per week) 0
      Midterm Examination (1 week)133
      Final Examination(1 week)133
      Preparation for Midterm Examination166
      Preparation for Final Examination11010
      Assignment / Homework/ Project22040
      Seminar / Presentation133
      Total Workload: 107
      ECTS Credit (Total workload/25): 4