Foundations of Machine Learning: Algorithms and Statistical Inference

Machine learning is the driving force behind modern technology, but truly mastering it requires understanding the core statistical principles that make algorithms work. This text-only course bridges the gap between theory and practice, helping you build a strong intuitive and mathematical foundation. You will transition from simply writing code to deeply understanding how, why, and when different machine learning models succeed. You will learn to formulate problems statistically, select the right algorithms for your data, and apply modern evaluation techniques to ensure reliable performance. What you'll learn: Understand the fundamental concepts of statistical inference and supervised learning; Implement core classification and regression algorithms, including linear models and support vector machines; Explore advanced ensemble methods like boosting and probabilistic models such as Bayesian networks; Apply modern evaluation metrics and cross-validation techniques to prevent overfitting; Analyze real-world data patterns using structured machine learning workflows. The course begins with foundational terminology, basic statistical concepts, and linear models before progressing to complex algorithms, probabilistic graphical models, and modern evaluation strategies. Through structured readings and conceptual exercises, you will develop a rigorous framework for solving predictive problems. This course is designed for aspiring data scientists, engineers, and analytical thinkers who are new to machine learning and want a comprehensive, conceptually grounded introduction without needing advanced prior knowledge. Start your journey into the mathematical and practical world of machine learning today.