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;
|
| 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 | They acquire the skills to understand, explain, and use the basic concepts and methods of economics. | |||||
| 2 | Acquires macro-economic analysis skills. | |||||
| 3 | Acquire microeconomic analysis skills. | |||||
| 4 | Understands the formulation and implementation of economic policies at local, national, regional and/or global levels. | |||||
| 5 | Learn different approaches to the economy and economic issues. | |||||
| 6 | Learn qualitative and quantitative research techniques in economic analysis. | X | ||||
| 7 | Improving the ability to use modern software, hardware and/or other technological tools. | X | ||||
| 8 | Develops intra-disciplinary and inter-disciplinary team work skills. | |||||
| 9 | Contributes to open-mindedness by encouraging critical analysis, discussion, and/or lifelong learning. | |||||
| 10 | Develops a sense of work ethics and social responsibility. | |||||
| 11 | Develops communication skills. | |||||
| 12 | Improving the ability to effectively apply knowledge and skills in at least one of the following areas: Economic policy, public policy, international economic relations, industrial relations, monetary and financial relations | |||||
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 | ||