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: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3500 and MATH 2210Q.
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: 18-AUG-22 05.20.21.840943 AM
|Term||Class Number||Campus||Instruction Mode||Instructor||Section||Session||Schedule||Enrollment||Location||Credits||Grading Basis||Notes|
|1223 16057 1 001||Spring 2022||16057||Storrs||Distance Learning||He, Suining||001||Reg||MoWe 4:40pm‑5:55pm
||8/10||No Room Required - Online||3.00||Graded|
|1228 8406 1 001||Fall 2022||8406||Storrs||In Person||Rajasekaran, Sanguthevar||001||Reg||TuTh 3:30pm‑4:45pm
|1228 14590 1 010X||Fall 2022||14590||Storrs||Online||Wei, Wei||010X||Reg||12:00am‑12:00am
||17/20||No Room Required - Online||3.00||Graded|