Preface
Introduction
The Machine Learning 1 course is a fundamental teaching unit aimed at introducing students to the essential foundations of Machine Learning. In the context of the growing influence of data-driven technologies, this course offers a comprehensive initiation into key Machine Learning concepts, methods, and algorithms. Students will explore both the theoretical underpinnings and practical implementations of machine learning, covering supervised and unsupervised learning techniques, with a particular emphasis on hands-on experience using popular libraries such as Scikit-Learn and Pandas. The course is designed for students specializing in Data Science and will serve as a cornerstone for more advanced studies in artificial intelligence, deep learning, and data-driven decision-making.
Objectives
The primary objectives of the course are:
- To understand the fundamental principles and types of Machine Learning (supervised, unsupervised, semi-supervised, reinforcement learning, and self-supervised learning).
- To master essential algorithms such as linear and logistic regression, decision trees, support vector machines (SVM), clustering methods, and association rule learning.
- To acquire practical skills in data preprocessing, feature engineering, dimensionality reduction, and anomaly detection.
- To develop proficiency in implementing machine learning models using Python libraries like Scikit-Learn, and to evaluate their performance critically.
- To prepare students for applying Machine Learning methods to real-world problems across various domains.
Prerequisites
To succeed in this course, students are expected to have:
- A strong background in mathematics, including linear algebra.
- A solid understanding of basic probability theory and statistics.
- Programming experience, particularly in Python, including familiarity with libraries such as NumPy and Pandas.