ECTS - Machine Learning
Machine Learning (ECON555) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Machine Learning | ECON555 | General Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Ph.D. |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| Course Objectives | This course provides an understanding of the application of software technologies that enables users to make better and faster decisions based on big data features. This course covers the a broad introduction to machine learning and statistical pattern recognition. |
| 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 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 | Naive Bayes. Laplace Smoothing. Kernel Methods. Discussion Section: Python | Lecture notes available |
| 7 | SVM. Kernels. | Lecture notes available |
| 8 | Neural Network. Discussion Section: Learning Theory | Lecture notes available |
| 9 | Bias/ Variance. Regularization. Feature/ Model selection. Discussion Section: Evaluation Metrics | Lecture notes available |
| 10 | Practical Advice for ML projects | Lecture notes available |
| 11 | K-means. Mixture of Gaussians. Expectation Maximization. | Lecture notes available |
| 12 | GMM(EM). Factor Analysis. | Lecture notes available |
| 13 | Principal Component Analysis. Independent Component Analysis | Lecture notes available |
| 14 | MDPs. Bellman Equations. Value iteration and policy iteration | Lecture notes available |
Sources
| Other Sources | 1. Ders Notlar / Lecture notes available |
|---|
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | 14 | 10 |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | 2 | 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 | To compare main theories and/or approaches in political economy and make a critical evaluation of each | X | ||||
| 2 | To compare main macroeconomic theories and/or approaches and make a critical evaluation of each | X | ||||
| 3 | To use complementary approaches from other relevant disciplines (e.g. political science, sociology) in order to solve problems requiring scientific expertise | X | ||||
| 4 | To develop the skills for establishing a micro-macro link in human and social sciences | X | ||||
| 5 | To analyze the main economic indicators and comment on them | X | ||||
| 6 | To acquire theoretical knowledge through literature survey and derive empirically testable hypothesis | X | ||||
| 7 | To be able to develop new approaches/theories for complex problems in political economy | X | ||||
| 8 | To apply critical thinking, statistical/econometric tools or other relevant quantitative and qualitative tools to new areas/problems | X | ||||
| 9 | To make a research design and carry it out within predetermined time frames | X | ||||
| 10 | To formulate and present policy recommendations based on academic research | X | ||||
| 11 | To continue learning and undertake advanced research independently | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 14 | 3 | 42 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 3 | 42 |
| Presentation/Seminar Prepration | 1 | 21 | 21 |
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 20 | 20 |
| Prepration of Final Exams/Final Jury | 1 | 25 | 25 |
| Total Workload | 150 | ||