Pending Additions and Revisions for Bioinformatics Algorithms, Computational Methods in Optimization, Computing for Science and Engineering, Statistical Machine Learning.
New Title and Course Description:
CS 52000: Computational Methods in Optimization
Prerequisites: MA 35100 or 51100 and CS 15900 or 17700.
A treatment of numerical algorithms and software for optimization problems with a secondary emphasis on linear and nonlinear systems of equations: unconstrained and constrained optimization; line search methods; trust region methods; Quasi-Newton methods; linear programming; calculating derivatives; quadratic programming; global optimization, including simulated annealing.
CS 57800 Statistical Machine Learning
Prerequisites: Calculus, basic linear algebra and probability; or consent of instructor.
Important concepts, models, and algorithms in machine learning. Topics include classical supervised learning (e.g., regression and classification), unsupervised learning (e.g., principle component analysis and Kmeans), and recent developments in the machine learning field such as Gaussian processes and message passing methods. While this course will give students the basic ideas and intuition behind modern machine learning methods, the underlying theme in the course is probabilistic inference on graphical models.
CS 57900: Bioinformatics Algorithms
Prerequisites: High school biology, CS 38100, and programming experience in C++, C, or Java.
Review of Genomes, DNA, RNA, proteins, proteomes. Biological Sequences: dynamic programming; pairwise global, local, and semi-global alignments of genes and proteins; constant, affine, and general gap penalties; RNA alignment; BLOSUM and PAM scoring matrices. Multiple alignment of proteins: approximation algorithms; iterative and progressive alignment methods. Database search for sequences: BLAST and variants. Phylogenetic Trees: distance-based methods, ultrametric and additive distance functions; parsimony, and maximum likelihood methods. Whole Genome Alignment: suffix trees and suffix arrays. Systems Biology: Module discovery in biological networks, spectral algorithms for graph clustering. Network alignment: quadratic programming formulations and graph matching. Genetic Variation: haplotype inference, the perfect phylogeny problem and chordal graphs. Additional topics such as next-generation sequencing, analysis of multidimensional data from flow cytometry, and gene expression data, if time permits.