ECTS - Machine Learning
Machine Learning (ECON484) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Machine Learning | ECON484 | General 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 | Has the ability to apply scientific knowledge gained in the undergraduate education and to expand and extend knowledge in the same or in a different area | |||||
| 2 | Can apply gained knowledge and problem solving abilities in inter-disciplinary research | |||||
| 3 | Has the ability to work independently within research area, to state the problem, to develop solution techniques, to solve the problem, to evaluate the obtained results and to apply them when necessary | |||||
| 4 | Takes responsibility individually and as a team member to improve systematic approaches to produce solutions in unexpected complicated situations related to the area of study | |||||
| 5 | Can develop strategies, implement plans and principles on the area of study and can evaluate obtained results within the framework | |||||
| 6 | Can develop and extend the knowledge in the area and to use them with scientific, social and ethical responsibility | |||||
| 7 | Has the ability to follow recent developments within the area of research, to support research with scientific arguments and data, to communicate the information on the area of expertise in a systematically by means of written report and oral/visual presentation | |||||
| 8 | To have an oral and written communication ability in at least one of the common foreign languages ("European Language Portfolio Global Scale", Level B2) | |||||
| 9 | Has software and hardware knowledge in the area of expertise, and has proficient information and communication technology knowledge | |||||
| 10 | Follows scientific, cultural, and ethical criteria in collecting, interpreting and announcing data in the research area and has the ability to teach. | |||||
| 11 | Has professional ethical consciousness and responsibility which takes into account the universal and social dimensions in the process of data collection, interpretation, implementation and declaration of results in mathematics and its applications. | |||||
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 | ||