ALGORITHMS FOR DATA SCIENCE
Methods for organizing data, e.g. hashing, trees, queues, lists,priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. Basic graph models and algorithms for searching, shortest paths, and matching. Dynamic programming. Linear and convex programming. Floating point arithmetic, stability of numerical algorithms, Eigenvalues, singular values, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Conjugate gradient, Newton and quasi-Newton methods. Large scale applications from signal processing, collaborative filtering, recommendations systems, etc.
- TR 11:40 a.m.-12:55 p.m.
- TR 1:10 p.m.-2:25 p.m.
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Instructor:
Eleni Drinea
CULPA: 8 Info - 3 points
TOPICS IN COMPUTER SCIENCE
ELEMENTS FOR DATA SCIENCE
Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.
- T 7 p.m.-9:30 p.m.
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Instructor:
Andi Cupallari
Info - 3 points
TOPICS IN COMPUTER SCIENCE
APPLIED DEEP LEARNING
Selected topics in computer science. Content and prerequisites vary between sections and semesters. May be repeated for credit. Check “topics course” webpage on the department website for more information on each section.
- T 7 p.m.-9:30 p.m.
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Instructor:
Andrei A Simion
Info - 3 points