In this course, you’ll learn theoretical foundations of optimization methods used for training deep machine learning models. Why does gradient descent work? Specifically, what can we guarantee about ...

Conjugate gradient methods form a class of iterative algorithms that are highly effective for solving large‐scale unconstrained optimisation problems. They achieve efficiency by constructing search ...

The rise of AI, graphic processing, combinatorial optimization and other data-intensive applications has resulted in data-processing bottlenecks, as ever greater amounts of data must be shuttled back ...

A thorough understanding of Linear Algebra and Vector Calculus, and strong familiarity with the Python programming language (e.g., basic data manipulation libraries, how to construct functions and ...

Existing high-dimensional motion planning algorithms are simultaneously overpowered and underpowered. Indomains sparsely populated by obstacles, the heuristics used by sampling-based planners to ...