Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
Explores the history, models, training, convergence, and limitations of neural networks, including the backpropagation algorithm and universal approximation.
Introduces the Machine Learning Programming course, covering MATLAB programming prerequisites and basics in Machine Learning, along with grading scheme and course materials.