Machine Learning (CMPE565) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Machine Learning CMPE565 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Computer Engineering Elective Courses
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives The objective of this course is to teach machine learning concepts and algorithms.
Course Learning Outcomes The students who succeeded in this course;
  • The objective of this course is to teach machine learning concepts and algorithms.
  • Design, implement, and test a machine learning (ML) system that learn and adapt using real-world applications
  • Select an optimal machine learning algorithm given to a specific problem
Course Content Concept learning, decision tree learning, artificial neural networks, evaluating hypotheses, Bayesian learning, computational learning theory, instance-based learning, genetic algorithms, analytical learning, reinforcement learning.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction Chapter 1 (main text)
2 Concept Learning and the General-to-Specific Ordering Chapter 2
3 Decision Tree Learning Chapter 3
4 Artificial Neural Networks Chapter 4
5 Evaluating Hypotheses Chapter 5
6 Bayesian Learning Chapter 6
7 Computational Learning Theory Chapter 7
8 Instance-Based Learning Chapter 8
9 Genetic Algorithms Chapter 9
10 Learning Sets of Rules Chapter 10
11 Analytical Learning Chapter 11
12 Combining Inductive and Analytical Learning Chapter 12
13 Reinforcement Learning Chapter 13
14 Reinforcement Learning Chapter 13
15 Review
16 Review

Sources

Course Book 1. T.M. Mitchell, Machine Learning, McGraw-Hill, 1997
Other Sources 2. E. Alpaydin, Introduction to Machine Learning, MIT Press, 2004.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 2 25
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 35
Final Exam/Final Jury 1 40
Toplam 4 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses
Major Area Courses
Supportive Courses X
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems. X
2 Develop solutions using different technologies, software architectures and life-cycle approaches. X
3 An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices. X
4 An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements. X
5 Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects.
6 An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain.
7 An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering.
8 Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies.
9 An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions.
10 Promote the development, adoption and sustained use of standards of excellence for software engineering practices.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project
Report
Homework Assignments 2 5 10
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 15 15
Prepration of Final Exams/Final Jury 1 20 20
Total Workload 125