Semestre 1

The course  "Machine Learning" for Data Science and Analytics Master's students provides a comprehensive introduction to the core principles of machine learning, with a special focus on contrasting supervised and unsupervised learning paradigms. Designed for first-year master’s students in Data Science, the course aims to build a strong foundation in machine learning algorithms, statistical models, and practical applications. Through a blend of theoretical instruction and hands-on projects, students will explore how machines learn from data and make predictions or decisions without being explicitly programmed.

Course Objectives:

• Understand the Fundamentals: Grasp the basic concepts, terminology, and methodology of machine learning.

• Differentiate Learning Paradigms: Clearly distinguish between supervised learning and unsupervised learning.

• Explore Supervised Learning: Cover algorithms like linear regression, logistic regression, support vector machines, and neural networks, and understand their use in regression and classification tasks.

• Investigate Unsupervised Learning: Study algorithms like k-means clustering, hierarchical clustering,  and learn how they are applied.

• Develop Practical Skills: Gain hands-on experience by applying machine learning algorithms to real-world datasets, evaluating model performance, and tuning parameters.

By the end of the course, students will be equipped with the knowledge to select appropriate machine learning techniques for different types of data and problems, and will be prepared to advance to more specialized topics in machine learning and artificial intelligence.