Psychology | |||||
Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
School/Faculty/Institute | Faculty of Engineering | |||||
Course Code | COMP 462 | |||||
Course Title in English | Introduction to Machine Learning | |||||
Course Title in Turkish | Yapay Öğrenmeye Giriş | |||||
Language of Instruction | EN | |||||
Type of Course | Flipped Classroom | |||||
Level of Course | Introductory | |||||
Semester | Fall | |||||
Contact Hours per Week |
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Estimated Student Workload | 152 hours per semester | |||||
Number of Credits | 6 ECTS | |||||
Grading Mode | Standard Letter Grade | |||||
Pre-requisites | None | |||||
Expected Prior Knowledge | Prior knowledge in programming, probability and statistics. | |||||
Co-requisites | None | |||||
Registration Restrictions | Only Undergraduate Students | |||||
Overall Educational Objective | To learn the fundamentals of machine learning methods and how to design and implement intelligent systems to make prediction, classification, and regression. | |||||
Course Description | This course covers the fundamentals of machine learning approaches: Supervised learning, unsupervised learning, regression methods, outlier detection, feature analysis, validation and evaluation. | |||||
Course Description in Turkish | Bu ders yapay öğrenmede kullanılan temel yöntemleri içermektedir: Gözetimli ve gözetimsiz öğrenme, bağlanım yöntemleri, aykırılık tespiti, öznitelik analizi, geçerleme ve performans değerlendirmesi |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) identify and solve a complex engineering problem using machine learning techniques; 2) design a machine learning system to produce solutions; 3) present the results of a machine learning solution to a range of audiences; 4) recognize ethical and professional responsibilities in creating the machine learning system; 5) analyze and interpret the data used for the machine learning system; 6) acquire and apply new knowledge of machine learning techniques; |
Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|
1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | ||||||
2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | ||||||
3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | ||||||
4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | ||||||
5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | ||||||
6) Internalization and dissemination of professional ethical standards. | ||||||
7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | ||||||
8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | ||||||
9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | ||||||
10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | ||||||
11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | ||||||
12) Ability to acquire knowledge independently, and to plan one’s own learning. | ||||||
13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
N None | S Supportive | H Highly Related |
Program Outcomes and Competences | Level | Assessed by | |
1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
6) | Internalization and dissemination of professional ethical standards. | N | |
7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
Prepared by and Date | YASSINE DRIAS , February 2023 |
Course Coordinator | TUBA AYHAN |
Semester | Fall |
Name of Instructor | Asst. Prof. Dr. TUNA ÇAKAR |
Week | Subject |
1) | Introduction to Machine Learning Concepts |
2) | K Nearest Neighbor Algorithm |
3) | Training, Testing and Validating Machine Learning Systems |
4) | Clustering Techniques |
5) | Decision Trees (Part 1) |
6) | Decision Trees (Part 2) |
7) | Gradient Descent Algorithm and Linear Regression |
8) | Logistic Regression |
9) | Feature Selection and Extraction Techniques |
10) | Advanced Classification Methods |
11) | Artificial Neural Networks (Part 1) |
12) | Artificial Neural Networks (Part 2) |
13) | Deep Learning Methods (Part 1) |
14) | Deep Learning Methods (Part 2) |
15) | Final Examination Period. |
16) | Final Examination Period. |
Required/Recommended Readings | Introduction to Machine Learning, Ethem Alpaydın, MIT Press, 3rd Edition (2015) | |||||||||||||||
Teaching Methods | Flipped Classroom | |||||||||||||||
Homework and Projects | Assignments | |||||||||||||||
Laboratory Work | None | |||||||||||||||
Computer Use | Required | |||||||||||||||
Other Activities | None | |||||||||||||||
Assessment Methods |
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Course Administration |
driasy@mef.edu.tr Instructor’s office: 5th floor Phone number: 0 212 395 37 45 Office hours: After the lecture hours. E-mail address: driasy@mef.edu.tr Rules for attendance: No attendance required. Statement on plagiarism: YÖK Regulations http://3fcampus.mef.edu.tr/uploads/cms/webadmin.mef.edu.tr/4833_2.pdf |
Activity | No/Weeks | Hours | Calculation | ||||
No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
Course Hours | 14 | 1 | 3 | 1 | 70 | ||
Homework Assignments | 4 | 1 | 14 | 2 | 68 | ||
Midterm(s) | 1 | 10 | 2 | 2 | 14 | ||
Total Workload | 152 | ||||||
Total Workload/25 | 6.1 | ||||||
ECTS | 6 |