Machine Learning (CMPE565) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Machine Learning CMPE565 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type 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 X
Supportive Courses
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 knowledge of mathematics, science, and engineering. X
2 An ability to design and conduct experiments, as well as to analyse and interpret data. X
3 An ability to design a system, component, or process to meet desired needs. X
4 An ability to function on multi-disciplinary domains. X
5 An ability to identify, formulate, and solve engineering problems. X
6 An understanding of professional and ethical responsibility. X
7 An ability to communicate effectively. X
8 Recognition of the need for, and an ability to engage in life-long learning. X
9 A knowledge of contemporary issues. X
10 An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. X
11 Skills in project management and recognition of international standards and methodologies X
12 An ability to produce engineering products or prototypes that solve real-life problems. X
13 Skills that contribute to professional knowledge. X
14 An ability to make methodological scientific research. X
15 An ability to produce, report and present an original or known scientific body of knowledge. X
16 An ability to defend an originally produced idea. X

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