FOUNDATIONS OF MACHINE LEARNING FOR ARTIFICIAL INTELLIGENCE

Foundations of Machine Learning for Artificial Intelligence introduces the core principles and techniques that enable computers to learn from data and make intelligent decisions. The subject covers supervised and unsupervised learning, model training, evaluation methods, and fundamental algorithms such as regression, classification, clustering, and neural networks. It emphasizes mathematical intuition, data preprocessing, and performance optimization to build reliable predictive models. Learners explore real-world applications in automation, pattern recognition, and decision support systems, along with ethical and practical considerations in AI deployment. By connecting theoretical foundations with hands-on problem solving, the course equips learners to design, analyze, and implement machine learning solutions that support modern intelligent systems.