This course provides an introduction to the fundamental concepts and techniques of Machine Learning. Students will explore supervised and unsupervised learning methods, model evaluation, and practical applications of machine learning algorithms to real-world data. Emphasis is placed on both the theoretical foundations and the implementation of models using modern tools and frameworks.

General Objectives

At the end of this course, the student should be able to:

• Understand the key principles and algorithms of machine learning.

• Distinguish between supervised, unsupervised, and reinforcement learning approaches.

• Apply appropriate algorithms to solve classification, regression, and clustering problems.

• Evaluate and interpret model performance using standard metrics.

• Implement machine learning solutions using programming tools such as Python and relevant libraries.

• Critically analyze the strengths, limitations, and ethical implications of machine learning systems