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.
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
|Non-negative Matrix Factorization||00:20:00|
|New Algorithms for Non-negative Matrix Factorization and Beyond||01:00:00|
|Tensor Decompositions and Their Applications||01:00:00|
|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|
|Submit Your Assignment||00:00:00|
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