The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.
Prerequisites: Prerequisites: Open to graduate students in the CSE program, others with permission. Recommended preparation: CSE3500 and MATH2210. (RG5910)
Grading Basis: Graded
Focuses on data science and big data analytics. Introduces basic concepts of data science and analytics. Different algorithmic techniques employed to process data will be discussed. Specific topics include: Parallel and out-of-core algorithms and data structures, Rules mining, Clustering algorithms, Text mining, String algorithms, Data reduction techniques, and Learning algorithms. Applications such as motif search, k-locus association, k-mer counting, error correction, sequence assembly, genotype-phenotype correlations, etc. will be investigated.
Last Refreshed: 30-OCT-20 05.20.18.831626 AM
|Term||Class Number||Campus||Instruction Mode||Instructor||Section||Session||Schedule||Enrollment||Location||Credits||Grading Basis||Notes|
|1208 11083 1 001||Fall 2020||11083||Storrs||Distance Learning||Rajasekaran, Sanguthevar||001||Reg||TuTh 2:00pm‑3:15pm
Waitlist Spaces: 30
|No Room Required - Online||3.00||Graded||Combined with CSE 4502-001|
|1213 16083 1 001||Spring 2021||16083||Storrs||Distance Learning||He, Suining||001||Reg||MoWeFr 10:10am‑11:00am
||4/10||No Room Required - Online||3.00||Graded|