STATS 155: Statistical Methods in Computational Genetics
The computational methods necessary for the construction and evaluation of sequence alignments and phylogenies built from molecular data and genetic data such as microarrays and data base searches. How to formulate biological problems in an algorithmic decomposed form, and building blocks common to many problems such as Markovian models, multivariate analyses. Some software covered in labs (Python, Biopython, XGobi, MrBayes, HMMER, Probe). Prerequisites: knowledge of probability equivalent to
STATS 116,
STATS 202 and one class in computing at the
CS 106 level. Writing intensive course for undergraduates only. Instructor consent required. (WIM)
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Holmes, S. (PI)
THINK 3: Breaking Codes, Finding Patterns
Why are humans drawn to making and breaking codes? To what extent is finding patterns both an art and a science? Cryptography has been used for millennia for secure communications, and its counterpart, cryptanalysis, or code breaking, has been around for just slightly less time. In this course we will explore the history of cryptography and cryptanalysis including the Enigma code, Navajo windtalkers, early computer science and the invention of modern Bayesian inference. We will try our own hand at breaking codes using some basic statistical tools for which no prior experience is necessary. Finally, we will consider the topic of patterns more generally, raising such questions as why we impute meaning to patterns, such as Biblical codes, and why we assume a complexity within a pattern when it's not there, such as the coincidence of birthdays in a group.
Terms: Aut

Units: 4

UG Reqs: THINK, WAYAQR, WAYFR

Grading: Letter (ABCD/NP)
Filter Results: