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The focus of the course is to organize around algorithmic issues that arise in machine learning. Modern machine learning systems are often built on top of algorithms that do not have provable guarantees, and it is the subject of debate when and why they work. In this course, focus on designing algorithms whose performance we can rigorously analyze for fundamental machine learning problems.  

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

Introduction 00:05:00
Non-negative Matrix Factorization 00:20:00
New Algorithms for Non-negative Matrix Factorization and Beyond 01:00:00
Tensor Decompositions 00:30:00
Tensor Decompositions and Their Applications 01:00:00
Sparse Coding 00:20:00
Alternating Minimization via Approximate Gradient Descent 00:45:00
Learning Mixture Models 00:20:00
Method of Moments and Systems of Polynomial Equations 00:30:00
Linear Inverse Problems 00:10:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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