Introduction to Machine Learning
Undergraduate Course, CuCEng, 2025
Course Objectives
This course introduces the fundamental principles of machine learning and their real-world applications, enabling students to design and evaluate intelligent systems capable of learning from data.
Assessment
%40 Midterm (exam,tasks,etc.) + %60 Final (exam,tasks,etc.)
Prerequisites
It is expected that students have basic programming knowledge (Python or equivalent).
Weekly Schedule
| Week | Topics | Lesson |
|---|---|---|
| 1 | Introduction and Basic Concepts of Machine Learning | Lesson 1 |
| 2 | Supervised Learning – Distance Based Classification (K-Nearest Neighbors) | Lesson 2 |
| 3 | Supervised Learning – Probability Based Classification (Naïve Bayes) | Lesson 3 |
| 4 | Supervised Learning – Entropy Based Classification (Decision Trees: ID3, C4.5) | Lesson 4 |
| 5 | Supervised Learning – Lagrange Based Classification (Support Vector Machines) | Lesson 5 |
| 6 | Supervised Learning – Least Squares Based Regression (Ordinary Linear Regression) | Lesson 6 |
| 7 | Unsupervised Learning – Clustering (K-Means) | Lesson 7 |
| 8 | Midterm Exam | |
| 9 | Unsupervised Learning – Dimensionality Reduction (Principal Component Analysis) | Lesson 8 |
| 10 | Unsupervised Learning – Association Rules (Apriori Algorithm) | Lesson 9 |
| 11 | Reinforcement Learning (Q-Learning) | Lesson 10 |
| 12 | Deep Learning – Perceptron and Adaline | Lesson 11 |
| 13 | Deep Learning – Artificial Neural Networks (MLP and RBF) | Lesson 12 |
| 14 | Deep Learning – Convolutional and Recurrent Neural Networks (CNN and RNN) | Lesson 13 |
| 15 | Deep Learning – Transfer Learning and Transformers | Lesson 14 |
| 16 | Review for Final Exam |
Resources
Below you can find past exam papers.