ECTS - Machine Learning
Machine Learning (ECON484) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Machine Learning | ECON484 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | |
| Learning and Teaching Strategies | . |
| Course Lecturer(s) |
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| Course Objectives | The main contents are, supervised learning unsupervised learning ; learning theory ; reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing and evaluation of policies and programs. |
| Course Learning Outcomes |
The students who succeeded in this course;
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| Course Content | Supervised learning, unsupervised learning; learning theory; reinforcement learning and adaptive control; recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing and evaluation of policies and programs. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction and Basic Concepts | Lecture notes available |
| 2 | Supervised Learning Setup. Linear Regression. Discussion Section: Linear Algebra | Lecture notes available |
| 3 | Weighted Least Squares. Logistic Regression. Netwon's Method | Lecture notes available |
| 4 | Perceptron. Exponential Family. Generalized Linear Models. Discussion Section: Probability | Lecture notes available |
| 5 | Gaussian Discriminant Analysis | Lecture notes available |
| 6 | Midterm Exam | |
| 7 | Naive Bayes. Laplace Smoothing. Kernel Methods. Discussion Section: Python | Lecture notes available |
| 8 | SVM. Kernels. | Lecture notes available |
| 9 | Neural Network. Discussion Section: Learning Theory | Lecture notes available |
| 10 | Bias/ Variance. Regularization. Feature/ Model selection. Discussion Section: Evaluation Metrics | Lecture notes available |
| 11 | Practical Advice for ML projects | Lecture notes available |
| 12 | K-means. Mixture of Gaussians. Expectation Maximization. | Lecture notes available |
| 13 | GMM(EM). Factor Analysis. | Lecture notes available |
| 14 | Principal Component Analysis. Independent Component Analysis. | Lecture notes available |
| 15 | MDPs. Bellman Equations. Value iteration and policy iteration | Lecture notes available |
| 16 | Final Exam |
Sources
| Other Sources | 1. Ders Notları / Lecture notes available |
|---|
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | 15 | 10 |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | 1 | 20 |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 20 |
| Final Exam/Final Jury | 1 | 50 |
| Toplam | 18 | 100 |
| Percentage of Semester Work | |
|---|---|
| Percentage of Final Work | 100 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| 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 | Having the conceptual and applied knowledge about the basic functions of the business and gaining the ability to use the knowledge | |||||
| 2 | To be able to diagnose the problems related to the field of business, to use scientific approaches effectively in the solution of problems and decision-making processes. | |||||
| 3 | To be able to reveal and analyze the environmental, social, global effects and legal consequences of the applications related to the basic functions of the business. | |||||
| 4 | To be able to analyze information and reports that concern businesses at the national, regional and global level, to set strategic goals according to the results obtained | |||||
| 5 | Gain the ability to use, report and interpret Business Information Systems and sub-modules required by business management | |||||
| 6 | Planning necessary activities such as starting a new business with innovative and creative thinking and taking risks for the sustainability of the business, finding resources, making market analysis, preparing a business plan, and using the learned information in this direction. | |||||
| 7 | Being aware of the fact that the research and learning process continues throughout life, to be able to follow the scientific and technological developments related to the business, to support oneself and the organization in innovation and continuous development. | |||||
| 8 | To have the necessary leadership and managerial skills to achieve business objectives effectively and efficiently. | |||||
| 9 | To be able to conduct scientific research in the field of business and report the results of the research to be used in managerial decision-making processes. | |||||
| 10 | To be able to transfer information about the field of business using effective verbal, written and visual communication methods in the language of learning and professional English | |||||
| 11 | To be aware of professional ethics, environmental awareness, sustainability, social responsibility, cultural, social and universal values | |||||
| 12 | Working effectively with different disciplines or multicultural teams, taking responsibility, making risk analysis, adapting to change, critical thinking and using initiative in problem solving | |||||
| 13 | . | |||||
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 | 3 | 48 |
| Presentation/Seminar Prepration | 1 | 5 | 5 |
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 10 | 10 |
| Prepration of Final Exams/Final Jury | 1 | 15 | 15 |
| Total Workload | 126 | ||