This course provided a comprehensive foundation in the mathematical principles essential for machine learning and artificial intelligence. I gained a deep understanding of how random variables are described and how the chain rule is applied in machine learning contexts. The course covered probability and information theory, including random variables, chain rule of conditional probabilities, and properties of mathematical functions commonly used in machine learning.
I explored topics from linear algebra that are essential for machine learning tasks, learning how to work with vectors, matrices, and their operations. The course also covered numerical computations including gradient descent and constrained optimization, which are fundamental techniques for training machine learning models.
Key concepts I mastered include maximum likelihood estimation, regression techniques, classification evaluation, and dimensional reduction techniques. I also gained knowledge in Mathematical Game Theory and Markov chains, understanding how these concepts apply to AI systems and decision-making processes.
The course emphasized developing time-dependent, deterministic, and stochastic state space functions, which are crucial for modeling dynamic systems in AI. Through practical exercises, I learned to identify and develop solutions for Mathematical Game Theory problems and to work with dynamical systems including time-dependent functions, deterministic and stochastic state spaces, and evolution rules.
This course has equipped me with the mathematical foundation necessary to understand, implement, and optimize machine learning algorithms, providing the theoretical background needed for advanced AI research and development.