Visit for course dates & times.
Nota : Ceci est la version 2021鈥�2022 de l'annuaire 茅lectronique. Veuillez mettre 脿 jour l'ann茅e dans la barre d'adresse de votre navigateur pour une version plus r茅cente de cette page, ou .
Vue d'ensemble
Informatique (Sci) : Concentration inequalities, PAC model, VC dimension, Rademacher complexity, convex optimization, gradient descent, boosting, kernels, support vector machines, regression and learning bounds. Further topics selected from: Gaussian processes, online learning, regret bounds, basic neural network theory.
Terms: Hiver 2022
Instructors: Oberman, Adam (Winter)
Prerequisites: MATH 462 or COMP 451 or (COMP 551, MATH 222, MATH 223 and MATH 324) or ECSE 551.
Restrictions: Not open to students who have taken or are taking MATH 562. Not open to students who have taken COMP 599 when the topic was "Statistical Learning Theory" or "Mathematical Topics for Machine Learning". Not open to students who have taken COMP 598 when the topic was "Mathematical Foundations of Machine Learning".
