Course Details

ELE489 - Fundamentals of Machine Learning

2023-2024 Spring term information
The course is open this term
Name Surname Position Section
S. Esen Yüksel Supervisor 21
Weekly Schedule by Sections
Section Day, Hours, Place
21 Tuesday, 09:40 - 12:30, E6

Timing data are obtained using weekly schedule program tables. To make sure whether the course is cancelled or time-shifted for a specific week one should consult the supervisor and/or follow the announcements.

ELE489 - Fundamentals of Machine Learning
Program Theoretıcal hours Practical hours Local credit ECTS credit
Undergraduate 3 2 4 7
Obligation : Elective
Prerequisite courses : ELE320
Concurrent courses : -
Delivery modes : Face-to-Face
Learning and teaching strategies : Lecture, Question and Answer, Problem Solving, Programming
Course objective : This course provides an introduction to the theory and applications of machine learning. The goal is to provide students with a deep understanding of the subject matter and skills to apply these concepts to real world problems using Python as the programming language.
Learning outcomes : A student who completes the course successfully has both theoretical and practical knowledge on machine learning, and he/she can apply her knowledge to real-world problems. She knows about classification, regression, clustering, supervised and unsupervised techniques in handling data, and can select between several models depending on performance metrics.
Course content : Introduction to Machine Learning, Machine Learning Tools and Libraries in the Python Programming Language, k-Nearest Neighbors, Naïve Bayes Classifier, Maximum Likelihood Estimation, Decision Trees, Support Vector Machines, Perceptron, Neural Networks, Deep Learning, Auto-encoding and Self-supervision, Generative Adversarial Networks, Classification Performance Metrics, Model Selection, Dimension Reduction, Clustering, Regression, Ensemble Methods.
References : Ethem Alpaydin, Introduction to Machine Learning, The MIT Press, 2020 Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006. Edward Raff, Inside Deep Learning, Manning, 2022
Course Outline Weekly
Weeks Topics
1 Introduction to Machine Learning
2 Introduction to Python Programming: Machine Learning Tools and Libraries
3 Nearest Neighbor Classifier, Naïve Bayes Classifier, Maximum Likelihood
4 Classification Performance Metrics, Model Selection
5 Linear Regression
6 Dimension Reduction
7 Clustering
8 Decision Trees
9 Midterm
10 Support Vector Machines
11 Neural Networks
12 Deep Learning, Auto-encoding, Generative Adversarial Networks
13 Ensemble Methods: Bagging, Boosting
14 Project Presentations
15 Final exam preparation
16 Final exam
Assessment Methods
Course activities Number Percentage
Attendance 0 0
Laboratory 0 0
Application 0 0
Field activities 0 0
Specific practical training 0 0
Assignments 4 20
Presentation 0 0
Project 1 10
Seminar 0 0
Quiz 0 0
Midterms 1 30
Final exam 1 40
Total 100
Percentage of semester activities contributing grade success 60
Percentage of final exam contributing grade success 40
Total 100
Workload and ECTS Calculation
Course activities Number Duration (hours) Total workload
Course Duration 14 3 42
Laboratory 0 0 0
Application 0 0 0
Specific practical training 0 0 0
Field activities 0 0 0
Study Hours Out of Class (Preliminary work, reinforcement, etc.) 14 5 70
Presentation / Seminar Preparation 0 0 0
Project 1 30 30
Homework assignment 4 3 12
Quiz 0 0 0
Midterms (Study Duration) 1 25 25
Final Exam (Study duration) 1 40 40
Total workload 35 106 219
Matrix Of The Course Learning Outcomes Versus Program Outcomes
Key learning outcomes Contribution level
1 2 3 4 5
1. Possesses the theoretical and practical knowledge required in Electrical and Electronics Engineering discipline.
2. Utilizes his/her theoretical and practical knowledge in the fields of mathematics, science and electrical and electronics engineering towards finding engineering solutions.
3. Determines and defines a problem in electrical and electronics engineering, then models and solves it by applying the appropriate analytical or numerical methods.
4. Designs a system under realistic constraints using modern methods and tools.
5. Designs and performs an experiment, analyzes and interprets the results.
6. Possesses the necessary qualifications to carry out interdisciplinary work either individually or as a team member.
7. Accesses information, performs literature search, uses databases and other knowledge sources, follows developments in science and technology.
8. Performs project planning and time management, plans his/her career development.
9. Possesses an advanced level of expertise in computer hardware and software, is proficient in using information and communication technologies.
10. Is competent in oral or written communication; has advanced command of English.
11. Has an awareness of his/her professional, ethical and social responsibilities.
12. Has an awareness of the universal impacts and social consequences of engineering solutions and applications; is well-informed about modern-day problems.
13. Is innovative and inquisitive; has a high level of professional self-esteem.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
General Information | Course & Exam Schedules | Real-time Course & Classroom Status
Undergraduate Curriculum | Minor Program For Non-departmental Students | Open Courses, Sections and Supervisors | Weekly Course Schedule | Examination Schedules | Information for Registration | Prerequisite and Concurrent Courses | Legal Info and Documents for Internship | Academic Advisors for Undergraduate Program | Information for ELE 401-402 Graduation Project | Virtual Exhibitions of Graduation Projects | Erasmus+ Program | Program Educational Objectives & Student Outcomes | ECTS Course Catalog | HU Registrar's Office
Graduate Curriculum | Open Courses and Supervisors | Weekly Course Schedule | Final Examinations Schedule | Schedule of Graduate Thesis Defences and Seminars | Information for Registration | ECTS Course Catalog - Master's Degree | ECTS Course Catalog - PhD Degree | HU Graduate School of Science and Engineering