ECTS - Machine Learning
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 | Elective Courses Taken From Other Departments |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| 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;
|
| 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 | To be able to use mathematics, science and engineering knowledge in solving engineering problems related to information systems. | X | ||||
| 2 | Design and conduct experiments in the field of informatics, analyze and interpret the results of experiments. | X | ||||
| 3 | Designs an information system, component and process according to the specified requirements. | X | ||||
| 4 | Can work effectively in disciplinary and multidisciplinary teams. | |||||
| 5 | Identify, formulate and solve engineering problems in the field of informatics. | X | ||||
| 6 | Acts in accordance with professional ethical rules. | |||||
| 7 | Communicates effectively both orally and in writing. | |||||
| 8 | Gains awareness of the necessity of lifelong learning. | |||||
| 9 | Learn about contemporary issues. | X | ||||
| 10 | To be able to use modern engineering tools, techniques and skills required for engineering practice. | X | ||||
| 11 | Knows project management methods and recognizes international standards. | X | ||||
| 12 | Develop informatics-related engineering products and prototypes for real-life problems. | X | ||||
| 13 | Contributes to professional knowledge. | |||||
| 14 | Can do methodological scientific research. | |||||
| 15 | Produce, report and present a scientific work based on an original or existing body of knowledge. | |||||
| 16 | Can defend the original idea generated. | |||||
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 | ||