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.
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.
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Course Credit: MIT
|Fast Rates and VC Theory||00:10:00|
|The VC Inequality||00:15:00|
|Support Vector Machines||00:15:00|
|Projected Gradient Descent||00:10:00|
|Stochastic Gradient Descent||00:15:00|
|Prediction with Expert Advice||00:15:00|
|Follow the Perturbed Leader||00:20:00|
|Online Learning with Structured Experts||00:20:00|
|Prediction of Individual Sequences||00:15:00|
|Potential Based Approachability||00:20:00|
|Submit Your Assignment||00:00:00|
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