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.
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
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 | ||
Assessment | |||
Submit Your Assignment | 00:00:00 | ||
Certification | 00:00:00 |
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