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This course focus on machine learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. In this course is it provides a mathematically rigorous introduction to these developments with emphasis on methods and their analysis. Topics will be covered such as the statistical theory of machine learning, Algorithms, and Convexity.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

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Course Credit: MIT

Course Curriculum

Module: 01
Introduction 00:15:00
Binary Classification 00:15:00
Concentration Inequalities 00:15:00
Fast Rates and VC Theory 00:10:00
The VC Inequality 00:15:00
Module: 02
Covering Numbers 00:15:00
Chaining 00:15:00
Convexification 00:15:00
Boosting 00:15:00
Support Vector Machines 00:15:00
Module: 03
Gradient Descent 00:10:00
Projected Gradient Descent 00:10:00
Mirror Descent 00:10:00
Stochastic Gradient Descent 00:15:00
Prediction with Expert Advice 00:15:00
Module: 04
Follow the Perturbed Leader 00:20:00
Online Learning with Structured Experts 00:20:00
Stochastic Bandits 00:20:00
Prediction of Individual Sequences 00:15:00
Adversarial Bandits 00:20:00
Module: 05
Linear Bandits 00:20:00
Blackwell’s Approachability 00:20:00
Potential Based Approachability 00:20:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

Course Reviews

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