Machine Learning (ECON555) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Machine Learning ECON555 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language Turkish
Course Type N/A
Course Level Social Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
  • Dr. Dersin Öğretim Görevlisi
Course Assistants
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;
  • Upon the completion of this course, the student will be able to: Define and model the data structure under investigation;
  • use mathematical models and solve for equilibrium. Also models will be used analyze the policies related to various research field;
  • Students will learn the principles and best practices for how to use big data in order to support fact-based decision-making. Emphasis will be given to applications in various data which has big data facilities.
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 microeconomic theories, approaches and make a critical evaluation of each
2 To compare main macroeconomic theories, approaches and make a critical evaluation of each
3 To apply mathematical modeling X
4 To employ statistical and econometric tools in analyzing an economic phenomena X
5 To analyze the main economic indicators and comment on them X
6 To acquire theoretical knowledge through literature survey and derive empirically confirmable hypothesis X
7 To make a research design and carry it out within predetermined time frames X
8 To be able to develop new approaches for complex problems in applied economics and/or apply statistical/econometric tools to new areas/problems X
9 To formulate and present policy recommendations based on academic research X
10 To combine economic knowledge with other disciplines in order to solve problems requiring scientific expertise
11 To use information technology effectively X
12 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