Mathematical Foundations
Mathematical foundations play a crucial role in AI and ML algorithms. In this subheading, students delve into the mathematical concepts and techniques that underlie AI and ML. They explore linear algebra, calculus, probability theory, and optimization methods, understanding how these mathematical tools are applied in AI and ML algorithms. Through practical exercises, students gain proficiency in manipulating and analyzing mathematical models relevant to AI and ML.
Machine Learning Algorithms
The Machine Learning Algorithms session delves deeper into various machine learning algorithms, equipping students with a comprehensive understanding of their inner workings and practical applications. Students explore different types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. They gain hands-on experience in implementing and fine-tuning these algorithms to solve real-world problems.
Supervised Learning
In this subheading, students learn about supervised learning, where models are trained using labeled data. They explore popular algorithms such as linear regression, logistic regression, decision trees, and support vector machines. Students understand the principles behind these algorithms and their applicability in different domains, such as image classification, natural language processing, and recommendation systems. They also explore techniques for model evaluation and validation.
