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Data-Science/ Data-Driven-Science refers to an interdisciplinary field of scientific methods, processes, algorithms, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, like data mining. This self-contained research-oriented [course_title] aims to make you enable to extract information from data, let you know the Principal Component Analysis, Manifold Learning and Diffusion Maps, Spectral Clustering and a guarantee for its performance, Clustering on random graphs etc.


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

Course Curriculum

Overview and Two Open Problems 00:15:00
Principal Component Analysis in High Dimensions and the Spike Model 00:30:00
Graphs, Diffusion Maps, and Semi-supervised Learning 00:30:00
Spectral Clustering and Cheeger’s Inequality 00:45:00
Concentration Inequalities, Scalar and Matrix Versions 00:30:00
Johnson-Lindenstrauss Lemma and Gordon’s Theorem 00:45:00
Local Convergence of Graphs and Enumeration of Spanning Trees 00:30:00
Compressed Sensing and Sparse Recovery 00:30:00
Group Testing and Error-Correcting Codes 00:30:00
Approximation Algorithms and Max-Cut 00:45:00
Community Detection and the Stochastic Block Model 00:30:00
Synchronization Problems and Alignment 00:45:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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