Principles of Machine Learning, Explore core ML concepts and algorithms like supervised learning, concept learning, Find-S, and Candidate Elimination.
Course Description
This course offers a comprehensive introduction to the fundamental concepts and algorithms that form the backbone of machine learning. It focuses on designing effective learning systems and understanding the theoretical underpinnings that make machine learning possible.
Students will begin with an overview of machine learning, exploring its applications, types, and real-world impact. The course then delves into the essential components of learning systems, emphasizing what makes a learning problem well-posed and how to frame learning tasks appropriately.
Key algorithms such as Find-S and Candidate Elimination will be studied in detail, offering insights into hypothesis space search and version space representation. Learners will also explore the Decision Tree algorithm, understanding how machines make structured decisions based on data-driven patterns and logic.
By the end of this course, students will:
- Grasp the foundational ideas of machine learning and its types.
- Design and structure well-posed learning problems.
- Implement and analyze the Find-S and Candidate Elimination algorithms.
- Construct decision trees and evaluate their performance.
- Develop a solid conceptual framework for understanding more advanced ML models.
Designed for beginners and early-stage learners, this course builds a strong theoretical and practical base for further exploration in machine learning and artificial intelligence. It encourages curiosity, experimentation, and applied understanding through hands-on examples.
