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 Coordinator
Course Lecturer(s)	Specialist Bora Güngören
Course Assistants
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; Upon the completion of this course, the student will be able to: 1. Define and model the data structure under investigation; 2. use mathematical models and solve for equilibrium. Also models will be used analyze the policies related to various research field. 3. Duruma dayalı karar almayı desteklemek için büyük verilerin nasıl kullanılacağına ilişkin ilkeleri öğrenecektir.
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
#	Program Qualifications / Competencies	1	2	3	4	5
1	Acquires skills to use the advanced theoretical and applied knowledge obtained at the mathematics bachelors program to do further academic and scientific research in both mathematics-based graduate programs and public or private sectors.
2	Transplants and applies the theoretical and applicable knowledge gained in their field to the secondary education by using suitable tools and devices.
3	Acquires the skill of choosing, using and improving problem solving techniques which are needed for modeling and solving current problems in mathematics or related fields by using the obtained knowledge and skills.
4	Acquires analytical thinking and uses time effectively in the process of deduction.
5	Acquires basic software knowledge necessary to work in the computer science related fields and together with the skills to use information technologies effectively.
6	Obtains the ability to collect data, to analyze, interpret and use statistical methods necessary in decision making processes.
7	Acquires the level of knowledge to be able to work in the mathematics and related fields and keeps professional knowledge and skills up-to-date with awareness in the importance of lifelong learning.
8	Takes responsibility in mathematics related areas and has the ability to work affectively either individually or as a member of a team.
9	Has proficiency in English language and has the ability to communicate with colleagues and to follow the innovations in mathematics and related fields.
10	Has the ability to communicate ideas with peers supported by qualitative and quantitative data.
11	Has professional and 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