Iğdır University Lifelong Learning Programme

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type of Course Unit	Year of Study	Semester	Number of ECTS Credits
190105000107	FUNDAMENTALS OF MACHINE LEARNING	Elective	4	7	6

Level of Course Unit

First Cycle

Objectives of the Course

Introduce students to core machine learning concepts and techniques; cover supervised and unsupervised learning methods along with model evaluation, optimization, and regularization; and develop practical implementation skills through laboratory and coding assignments.

Name of Lecturer(s)

Dr. Öğr. Üyesi Fesih KESKİN

Learning Outcomes

1	Supervised Learning Techniques: Formulate and implement linear/logistic regression and decision tree algorithms for real-world problems.
2	Model Evaluation & Regularization: Analyze and optimize model performance using cross-validation, metrics, and regularization methods.
3	Unsupervised Learning & Dimensionality Reduction: Apply clustering algorithms (K-Means, hierarchical) and PCA to extract insights from unlabeled data.

Mode of Delivery

Daytime Class

Prerequisites and co-requisities

Basic Linear Algebra Probability & Statistics

Recommended Optional Programme Components

Weekly coding-focused labs and short homework assignments to reinforce theory

Course Contents

Introduction to machine learning and development environments; fundamental statistics and probability concepts; linear regression and least squares; logistic regression and classification problems; loss functions and optimization (gradient descent); model evaluation, cross-validation, and performance metrics; overfitting and regularization techniques; decision trees and random forests; ensemble methods (bagging, boosting); support vector machines; unsupervised learning: clustering algorithms (K-Means, hierarchical clustering); dimensionality reduction (PCA); introduction to neural networks and the backpropagation algorithm; advanced topics: overview of deep learning and example applications.

Weekly Detailed Course Contents

Week	Theoretical	Practice	Laboratory
1	Introduction: What is ML? Setup of Python/Scikit-Learn environment
2	Fundamental statistics & probability; data preprocessing (scaling, missing values) (lab)
3	Linear regression and least squares method (lab)
4	Logistic regression and binary classification (lab)
5	Loss functions & optimization: gradient descent and learning-rate selection (lab)
6	Model evaluation: train/test split, cross-validation, R², accuracy, ROC-AUC (lab)
7	Overfitting vs. underfitting; regularization techniques (L1, L2, dropout) (lab)
8	Midterm Exam
9	Decision trees & random forests: structure, hyperparameters, feature importance (lab)
10	Ensemble methods: bagging and boosting (AdaBoost, Gradient Boosting) (lab)
11	Support Vector Machines: kernel methods and parameter tuning (lab)
12	Unsupervised learning: K-Means and hierarchical clustering (lab)
13	Dimensionality reduction: Principal Component Analysis (PCA) (lab)
14	Introduction to neural networks: perceptrons, MLPs, and backpropagation (lab)
15	Final Exam
16	Final Exam

Recommended or Required Reading

Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow Christopher M. Bishop, Pattern Recognition and Machine Learning Hastie, Tibshirani & Friedman, The Elements of Statistical Learning Lecture notes, example code, and Kaggle datasets (provided by the instructor)

Planned Learning Activities and Teaching Methods

Assessment Methods and Criteria

Term (or Year) Learning Activities	Quantity	Weight
Midterm Examination	1	100
SUM		100
End Of Term (or Year) Learning Activities	Quantity	Weight
Final Examination	1	100
SUM		100
Term (or Year) Learning Activities		40
End Of Term (or Year) Learning Activities		60
SUM		100

Language of Instruction

Turkish

Work Placement(s)

No mandatory internship.

Workload Calculation

Activities	Number	Time (hours)	Total Work Load (hours)
Midterm Examination	1	1	1
Final Examination	1	1	1
Attending Lectures	14	1	14
Practice	5	3	15
Self Study	16	5	80
Individual Study for Mid term Examination	8	2	16
Individual Study for Final Examination	8	4	32
Homework	5	2	10
TOTAL WORKLOAD (hours)			169

Contribution of Learning Outcomes to Programme Outcomes

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10	PO 11	PO 12	PO 13
LO1	5	5	5	4	4	5	4	4	4	4	4	5	4
LO2	5	4	4	5	4	5	5	5	4	5	5	5	5
LO3	4	4	5	5	4	5	5	4	4	4	4	4	5

* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High