BIO 450 : Conservation Biology.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall |
| PREREQUISITE: | BIO 220A & BIO 350; or BIO 220B & BIO 350 |
| DESCRIPTION:  | Scientific principles of conservation: applying population genetics, and phylogenetic and ecological theory to preservation of biological diversity; developing sustainable ecological systems compatible with human resource use. |
BIO 463 : Genetics of Human Disease.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Winter |
| PREREQUISITE: | PWS 340 |
| DESCRIPTION:  | Examining the application of genetics to understanding and treatment of human disease. Functional consequences of mutations; use of model organisms; linkage and association analysis of complex traits; pharmacogenetics; ethical considerations. |
BIO 468 : (Bio-MMBio-PWS) Genomics.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter |
| PREREQUISITE: | MMBIO 240 & PWS 340 |
| DESCRIPTION:  | Current analysis of genes and genomes; computational and statistical approaches for analyzing genomic data, including genome sequencing and annotation, gene expression and the transcriptome, proteomics and functional genomics, and genetic variation and SNPs. |
BIO 555 : Evolutionary and Ecological Modeling.
(2:2:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Winter Even Yrs. |
| PREREQUISITE: | Senior status in bioinformatics program or graduate status; Stat 511, 512, or equivalent; instructor's consent. |
| DESCRIPTION:  | Using models in ecology. Practical experience in analytical, simulation, and agent-based models. |
BIO 560 : Population Genetics.
(4:4:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Winter Odd Yrs. |
| PREREQUISITE: | Bio 420 or equivalent. |
| DESCRIPTION:  | Basic principles of population genetics applied to natural populations; drift, selection, and nonrandom mating; inferring population subdivision, migration, and gene flow. |
C S 312 : Algorithm Analysis.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter; Spring |
| PREREQUISITE: | C S 240 & C S 252 |
| DESCRIPTION:  | Analysis of algorithms including searching, sorting, graphs, and trees. |
C S 340 : Software Design and Testing.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter; Spring; Summer |
| PREREQUISITE: | C S 240 |
| DESCRIPTION:  | Principles of software design, design patterns, design representation, refactoring. Principles of software quality assurance and testing. Development and testing tools. |
C S 360 : Internet Programming.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter |
| PREREQUISITE: | C S 240 |
| DESCRIPTION:  | Internet application programming, including sockets, threads, CGI, database, e-commerce, Web services. |
C S 450 : Introduction to Digital Signal and Image Processing.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter |
| PREREQUISITE: | C S 312 & MATH 313 & STAT 121 |
| DESCRIPTION:  | One- and two-dimensional signal-processing fundamentals, including sampling, noise, transforms, filtering, enhancement, and compression. Hands-on experimentation with speech, music, still images, and full-motion video. |
C S 452 : Database Modeling Concepts.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall |
| PREREQUISITE: | C S 340 & C S 360 |
| DESCRIPTION:  | Database models: relational, deductive, object-oriented. Integrity constraints, query languages, database design. |
C S 470 : Introduction to Artificial Intelligence.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Spring |
| PREREQUISITE: | C S 312 & MATH 313 & STAT 121 |
| DESCRIPTION:  | Introduction to core areas of artifical intelligence; intelligent agents, problem solving and search, knowledge-based systems and inference, planning, uncertainty, learning, and perception. |
C S 478 : Tools for Machine Learning and Data Mining.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter |
| PREREQUISITE: | C S 312 & MATH 313 & STAT 121 |
| DESCRIPTION:  | Machine learning and data mining models and other mechanisms allowing computers to learn and find knowledge from data. |
C S 484 : Parallel Processing.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall |
| PREREQUISITE: | C S 360 |
| DESCRIPTION:  | Theoretical and practical study of parallel processing including multi-core, fine-grained, and clustered architectures, parallel programming languages, and parallel algorithms. |
CHEM 353 : Organic Chemistry Laboratory--Nonmajors.
(1-2:0:6)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter; Spring; Summer |
| PREREQUISITE: | Chem 352 or concurrent enrollment (preferred). |
| DESCRIPTION:  | Physical and chemical properties, isolation and purification, characterization, syntheses. |
| NOTE: | For predentistry, premedicine, and other majors who do not intend to take Chem 455. |
CHEM 481 : Biochemistry.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter; Spring |
| PREREQUISITE: | CHEM 352M & PDBIO 120; or CHEM 352 & PDBIO 120 |
| DESCRIPTION:  | First-semester biochemistry. Molecular components of cells, chemical structure and function, enzymes, metabolic transformations, photosynthesis. |
| NOTE: | For chemistry majors and students in biological sciences who contemplate pursuing advanced degrees, including medicine. |
CHEM 482 : Mechanisms of Molecular Biology.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Winter |
| PREREQUISITE: | CHEM 481M; or CHEM 481 |
| DESCRIPTION:  | Second-semester biochemistry. Nucleic acid biochemistry and molecular biology: nucleotide metabolism, chromosome and chromatin structure, DNA structure and replication, RNA transcription and gene expression, protein synthesis and regulation, eukaryotic gene systems, signal transduction. |
CHEM 489 : Structural Biochemistry.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall |
| PREREQUISITE: | CHEM 468 |
| DESCRIPTION:  | Molecular structures of proteins, RNA and DNA as determinants of biological function. Topics include thermodynamics of folding and binding, structural determination, spectroscopy, modeling, protein recognition. |
CHEM 584 : Biochemistry Laboratory/Proteins.
(3:1:6)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter |
| PREREQUISITE: | CHEM 481M; or CHEM 481; or equivalent. |
| DESCRIPTION:  | Introduction to current biochemical research procedures including spectrophotometry, chromatography, electrophoresis, and immunological techniques. Protein over-expression; isolation and characterization methods. Enzyme kinetics and protein-ligand interactions. Introduction to bioinformatics. |
| NOTE: | May be taken before or after Chem 586. |
CHEM 586 : Biochemistry Laboratory/Nucleic Acids.
(3:1:6)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter |
| PREREQUISITE: | CHEM 482 |
| DESCRIPTION:  | Laboratory course covering major techniques involved in isolation, amplification, and cloning of recombinant DNA as well as isolation, synthesis, translation, and identification of RNA. |
| NOTE: | May be taken before or after Chem 584. |
MATH 334 : Ordinary Differential Equations.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter; Spring; Summer |
| PREREQUISITE: | MATH 113 & MATH 313 |
| DESCRIPTION:  | Methods and theory of ordinary differential equations. |
MATH 410 : Introduction to Numerical Methods.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall |
| PREREQUISITE: | MATH 314; C S 142 or equivalent. |
| DESCRIPTION:  | Root finding, interpolation, curve fitting, numerical differentiation and integration, multiple integrals, direct solvers for linear systems, least squares, rational approximations, Fourier and other orthogonal methods. |
MATH 411 : Numerical Methods.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Winter |
| PREREQUISITE: | MATH 334 & MATH 410 |
| DESCRIPTION:  | Iterative solvers for linear systems, eigenvalue, eigenvector approximations, numerical solutions to nonlinear systems, numerical techniques for initial and boundary value problems, elementary solvers for PDEs. |
MATH 431 : (Math - EC En 370) Probability Theory.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall |
| PREREQUISITE: | MATH 313 |
| DESCRIPTION:  | Axiomatic probability theory, conditional probability, discrete / continuous random variables, expectation, conditional expectation, moments, functions of random variables, multivariate distributions, laws of large numbers, central limit theorem. |
MMBIO 360 : Microbial Genetics.
(4:3:3)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall |
| PREREQUISITE: | MMBIO 240 |
| DESCRIPTION:  | How DNA governs complex bacterial functions. Classical and modern approaches to gene discovery, including genetic screens, gene mapping, targeted genetic manipulations, and analyzing gene activity. |
MMBIO 465 : Virology.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter |
| PREREQUISITE: | MMBio 261 or equivalent. |
| DESCRIPTION:  | Basic principles of virology, emphasizing selected molecular aspects of virus life cycles and disease processes. |
PDBIO 360 : Cell Biology.
(3:3:1)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter; Spring |
| PREREQUISITE: | MMBIO 240 |
| DESCRIPTION:  | Fundamentals of cell structure and function with reference to analytical methods used by cell biologists. Practice in designing, executing, and interpreting relative experiments. |
PDBIO 362 : Advanced Physiology.
(3:3:1)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter; Spring |
| PREREQUISITE: | MMBIO 240 & PHSCS 106; or MMBIO 240 & PHSCS 220 |
| DESCRIPTION:  | Integrated approach to organ system and cellular physiology. Problem solving/calculations. |
| NOTE: | Requires background in chemistry and molecular biology. Students without this background should take PDBio 305. |
PDBIO 482 : Developmental Biology.
(3:3:1)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall; Winter |
| PREREQUISITE: | MMBIO 240 & MMBIO 241 & PDBIO 360 |
| RECOMMENDED: | PDBio 325. |
| DESCRIPTION:  | Invertebrate and vertebrate developmental biology. Embryonic gastrulation, neurulation, pattering, etc. Modern approaches and research strategies. Emphasizes gene function, cell signaling, signal transduction during embryogenesis. |
PDBIO 582 : Developmental Genetics.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Winter |
| PREREQUISITE: | PDBio 482 or equivalent. |
| DESCRIPTION:  | Gene function and regulation during cell specification and differentiation, pattern formation, and organogenesis in developing embryo. |
STAT 341 : Statistical Theory 1.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall |
| PREREQUISITE: | MATH 314; Stat 121, 151, 201, or 301. |
| DESCRIPTION:  | Axiomatic probability theory for discrete and continuous random variables; moment-generating functions; conditional probability; stochastic independence; transformations; limiting distributions; stochastic convergence; central limit theorem. |
STAT 342 : Statistical Theory 2.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Winter |
| PREREQUISITE: | STAT 341 |
| DESCRIPTION:  | Sufficiency and completeness; point and interval estimation; hypothesis testing; Cramer-Rao inequality; some asymptotic results; Bayesian methods. |
STAT 424 : Statistical Computing 2.
(3:3:2)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Winter |
| PREREQUISITE: | STAT 224 & STAT 330 |
| DESCRIPTION:  | S Plus, statistical graphics, simulation, advanced SAS (macros, Proc IML, and Proc SQL), and database programming. |
STAT 431 : Experimental Design.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall |
| PREREQUISITE: | STAT 330 |
| DESCRIPTION:  | Basic designs, power and sample size, Latin squares, incomplete blocks, change-over designs, factorials, fractional factorials, confounding, split-plots, response surface designs. |
STAT 435 : Nonparametric Statistical Methods.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Winter |
| PREREQUISITE: | STAT 330; or STAT 511 |
| DESCRIPTION:  | Permutation tests, rank-based methods, analysis of contingency tables, bootstrap methods, curve fitting. |
STAT 535 : Applied Linear Models.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Fall |
| PREREQUISITE: | Departmental consent. |
| DESCRIPTION:  | Theory of estimation and testing in linear models. Analysis of full-rank model, over-parameterized model, cell-means model, unequal subclass frequencies, and missing and fused cells. Estimability issues, diagnostics. |
STAT 537 : Generalized Linear Models.
(3:3:0)(Credit Hours:Lecture Hours:Lab Hours)| WHEN TAUGHT: | Winter |
| PREREQUISITE: | STAT 535 & STAT 642 |
| DESCRIPTION:  | Generalized linear models framework, binary data, polytomous data, log-linear models. |