CMSC 498Y

Course Logistics

• Title: Statistical Inference and Machine Learning for Genomics Data
• Instructor: Erin Molloy
• Meetings: Tuesdays and Thursdays 9:30-10:45 AM in IRB-2207
• Materials: Available on ELMS (http://elms.umd.edu)
• Communication: CampusWire (https://campuswire.com) for questions about course logisitcs, material, assignments, exams, etc. Otherwise, please contact me via email, including "CMSC498Y" in the subject line.
Syllabus: Download the syllabus here: http://ekmolloy.github.io/downloads/cmsc498y-syllabus.pdf.

Course Description

The objective of this course is to provide an understanding of statistical and machine learning algorithms that have been developed or applied to problems in comparative genomics, for example building multiple sequence alignments, estimating phylogenies (evolutionary trees), and inferring population structure from single nucleotide polymorphisms (SNPs). Example topics include Hidden Markov Models (HMMs), Expectation-Maximization (EM), Markov Clustering (MCL), Principal Component Analysis (PCA), Matrix Factorization, Maximum Likelihood (ML) and Bayesian methods (including composite and pseudo-likelihood functions), and Markov Chain Monte Carlo (MCMC). This course (CMSC 498Y) is intended to be complementary to CMSC 423 Bioinformatic Algorithms, Databases, and Tools, which largely focuses on discrete algorithms (e.g., suffix trees, Burrows-Wheeler transform, FM-index, de Bruijn graphs, etc.); see the syllabus from Fall 2021 here: https://rob-p.github.io/CMSC423_F21/.

The target audience is upper-level undergraduate students majoring in computer science and/or other quantitative disciplines as well as graduate students in bioinformatics-related disciplines. The prerequisites are familiarity with probability, statistics, and linear algebra as well as programming ability in either C/C++ or Python. No prior knowledge of biology is assumed. Please contact me via email if you are unsure as to whether you should enroll in the course or if you don't exactly meet the prerequisites but are interested in enrolling. If you are not a CS major, please follow the instructions here: https://undergrad.cs.umd.edu/majornon-major-permissions.

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