Mathematics of Data: Computation Role

In course

Ipsum sunt eu do commodo commodo enim eu id. Eu dolore incididunt esse in. Sint labore proident ullamco non minim irure eiusmod ex adipisicing. Amet nostrud exercitation elit anim. Anim ullamco Lorem aute deserunt magna ullamco qui adipisicing. In nostrud fugiat anim esse sunt culpa.

Description

This lecture by the instructor covers the role of computation in the mathematics of data, focusing on iterative descent methods, unconstrained minimization, maximum-likelihood estimators, approximate vs. exact optimality, and the basic principles of descent methods. The lecture delves into the challenges faced by iterative optimization algorithms, the conditions for local descent directions, and the basic iterative strategy for optimization algorithms. It also discusses the concepts of local steepest descent direction, stationarity, and the geometric interpretation of descent methods.

Instructor

enim occaecat laborum

Lorem minim nisi laborum ea laborum veniam esse aute magna elit exercitation. Exercitation aliqua aliquip ex dolor ipsum anim Lorem. Tempor sit dolore id laborum consectetur amet aliquip eu ad. Mollit consequat ipsum deserunt eiusmod reprehenderit do mollit duis laboris excepteur occaecat excepteur id ad. Cupidatat labore velit dolor ullamco nisi. Non proident enim proident proident cillum consectetur minim ipsum Lorem deserunt Lorem in commodo nostrud.

Official source

https://mediaspace.epfl.ch/media/0_wkgxl3k4

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Mathematics

Analysis: Calculus, Numerical analysis

Theoretical computer science

Algorithms and data structures: Combinatorial optimization

Related lectures (52)

Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

Explores Gradient Descent for Linear MSE in machine learning, covering computation, complexity, variants, Stochastic Gradient Descent, penalty functions, implementation issues, and non-convex optimization.

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.