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 | Gains the ability to use knowledge by acquiring conceptual and practical understanding of all core functions of business. | |||||
| 2 | Identifies problems related to the field of business and effectively uses scientific approaches in problem-solving and decision-making processes. | |||||
| 3 | Demonstrates and analyzes the environmental, social, global impacts and legal consequences of practices related to core business functions. | |||||
| 4 | Analyzes information and reports relevant to businesses at national, regional, and global levels, and sets strategic goals based on the results obtained. | |||||
| 5 | Gains the ability to use, report, and interpret Business Information Systems and their sub-modules required for business management. | |||||
| 6 | Plans the necessary activities such as taking risks, securing resources, conducting market analysis, and preparing business plans for starting a new venture and ensuring its sustainability with innovative and creative thinking, and applies the acquired knowledge accordingly. | |||||
| 7 | Supports oneself and the organization in terms of innovation and continuous improvement, while being aware that the process of research and learning is lifelong and following scientific and technological developments related to business. | |||||
| 8 | Acquires the necessary leadership and managerial skills to achieve business objectives effectively and efficiently. | |||||
| 9 | Conducts scientific research in the field of business and reports the research findings to be used in managerial decision-making processes. | |||||
| 10 | Uses effective verbal, written, and visual communication methods to convey information related to the field of business in the language of instruction and professional English. | |||||
| 11 | Develops awareness of professional ethics, environmental sensitivity, sustainability, social responsibility, and cultural, societal, and universal values. | |||||
| 12 | Takes initiative in working effectively with different disciplines or multicultural teams, assuming responsibility, conducting risk analysis, adapting to change, and applying critical thinking and problem-solving skills | |||||
| 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 | ||