Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
5AIN311Foundations of Machine Learning3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course The subject matter of this undergraduate-level introductory course is to provide students a broad overview of many concepts and algorithms in ML and skills to apply these concepts to real world problems.
Course Content Overview of Machine Learning
Nearest Neighbor Classifier
Linear Regression, Least Squares
Machine Learning Methodology
Learning Theory
Probability and Linear Algebra Basics
Statistical Estimation: MLE, MAP, Naive Bayes Classifier
Linear Classification Models: Logistic Regression, Linear Discriminant Functions, Perceptron
Artificial Neural Networks
Support Vector Machines
Decision Tree Learning
Ensemble Methods: Bagging, Boosting
Clustering
Ethics in Machine Learning
Course Methods and Techniques Lecture, Problem Solving
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 ) and ( AIN313 )
Course Coordinator None
Name of Lecturers Associate Prof. Erkut Erdem
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources 1. Mitchell T., Machine Learning, McGraw Hill, 1997. 2. Artificial Intelligence: A Modern Approach (3rd Edition), Stuart Russell and Peter Norvig. Prentice Hall, 2009. ? 3. Bayesian Reasoning and Machine Learning, David Barber, Cambridge University Press, 2012. ? 4. Introduction to Machine Learning (3rd Edition), Ethem Alpaydin, MIT Press, 2014. ? 5. Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press, 2012. 6. Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2006.
Course Notes Mitchell T., Machine Learning, McGraw Hill, 1997.
Artificial Intelligence: A Modern Approach (3rd Edition), Stuart Russell and Peter Norvig. Prentice Hall, 2009.
Bayesian Reasoning and Machine Learning, David Barber, Cambridge University Press, 2012.
Introduction to Machine Learning (3rd Edition), Ethem Alpaydin, MIT Press , 2014.
Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT Press, 2012.
Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2006


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 1 % 30
Attendance 1 % 5
Project 1 % 25
Final examination 1 % 40
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 4 56
Project 1 45 45
Preparation for Midterm Exam 1 15 15
General Exam Preparation 1 20 20
Total Work Load   Number of ECTS Credits 5,93333333333333 178

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 After completing the course, the students, learn about the basic concepts in main machine learning, learn basic machine learning algorithms such as decision trees, Bayesian methods, artificial neural networks, support vector machines.
2  
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Overview of Machine Learning, Nearest Neighbor Classifier
2 Linear Regression, Least Squares
3 Machine Learning Methodology, Learning Theory, Probability and Linear Algebra Basics
4 Statistical Estimation: MLE, MAP, Naive Bayes Classifier
5 Linear Classification Models: Logistic Regression, Linear Discriminant Functions, Perceptron
6 Artificial Neural Networks
7 Deep Neural Networks
8 Midterm exam
9 Support Vector Machines
10 Kernels, Kernel Trick for SVMs, Decision Tree Learning
11 Ensemble Methods: Bagging, Boosting
12 Clustering
13 Ethics in Machine Learning
14 Course wrap-up, Project Presentations
15 Final exam preparation
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 2 4 5 5 4 4 4 4 2 5
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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