Course Schedule

Fall 2016 Courses

Core Courses

Description: The algorithmic and machine learning foundations of computational biology, combining theory with practice are covered. Principles of algorithm design and core methods in computational biology, and an introduction of important problems in computational biology. Hands on experience analyzing large-scale biological data sets.

Prereq: Fundamentals of programming and algorithm design (EK 127 or equivalent), basic molecular biology (BE 209 or equivalent), statistics and probability (BE 200 or equivalent), or consent of instructor.

Instructor: Galagan; Credits: 4; Tues/Thurs, 1 pm – 3 pm and Fri, 10 am – 11:30 am

Description: Introduction to statistical methods relevant to research in the computational sciences. Core topics include probability theory, estimation theory, hypothesis testing, linear models, GLMs, and experimental design. Emphasis on developing a firm conceptual understanding of the statistical paradigm through data analyses.  Required for entering Bioinformatics Ph.D. students.

Prereq: MA 225 & MA 242 or their equivalents.

Instructor: TBA; Credits: 4; Tues/Thurs, 9:30 am – 11 am; DIS: Mon, 1-2 pm or 2-3 pm

MSSP only – Instructor: Wright; Credits: 4; Mon/Wed/Fri, 1 pm – 2 pm; DIS: Mon 2 pm – 3 pm

Project course for first year Bioinformatics Ph.D. students. Open-ended problems will involve bioinformatics as a key element, typically requiring the use of large data sets and computational analysis to make predictions about molecular function, molecular interactions, regulation, etc. Projects will be proposed by the Bioinformatics program faculty and selected by student in teams of three. The end result will be a set of predictions, some of which can be validated retrospectively using data available through online sources and some of which will require experimental validation. During the last 2 months of the academic year, teams will design feasible validation experiments in consultation with the experimental faculty.

Instructor: Benson; Credits: 2; Wed, 12:00 pm – 1:30 pm

This course is for PhD students to take part in a laboratory rotation system. Students will become familiar with research activity in Bioinformatics labs. These rotations will help students identify the laboratory in which they will perform their dissertation research. PhD students must complete one 9-week rotation in their first semester of matriculation and two in their second semester.

Instructor: Mohr; Credit: 1; ARR

Required for entering Bioinformatics Ph.D. students. The course will consist of a series of presentations by Bioinformatics faculty that focus on research projects being investigated in their laboratories. Emphasis is placed on the description of collaborative projects involving experimental and computational approaches to Bioinformatics research problems.

Instructor: TBA; Credits: 1; Mon, 3 pm – 5 pm 

In this course, the students present advanced papers in Computational Biology and Bioinformatics. The papers are chosen to cover recent breakthroughs in genomics, computational biology, high-throughput biology, analysis methods, computational modeling, databases, theory and bioinformatics.

Instructor: Segrè; Credits: 2; Wed, 2:00 pm – 4:00 pm

Synthesis, structure, and function of biologically important macromolecules (DNA, RNA, and proteins). Regulation and control of the synthesis of RNA and proteins. Introduction to molecular biology of eukaryotes. Discussion of molecular biological techniques, including genetics and recombinant DNA techniques. Three hours lecture, one hour discussion.

Instructor: Loechler; Credits: 4; LEC: Tues/Thurs 3:30 pm – 5:00 pm; DISC: Tues 5-6 pm, Wed 10-11 am, 2-3 pm, or 3-4 pm, Thurs 5-6 pm 

This course enrolls students who intend to pursue careers in medicine, dental medicine and/or medical research (either academic or industrial) – in particular, students in the BF for Translational Medicine track of the Bioinformatics MS program. After commencing briefly with general introductory material (published reviews and other relevant back-ground information), students will proceed to examine, discuss and evaluate recent papers that directly illustrate the use of bioinformatics either in pre-clinical or clinical research settings. Papers will be drawn from high-impact journals such as Nature, Science, PNAS, Cell, and Science Translational Medicine. Students will take turns presenting the papers to the class and provide a critical review of each, both orally and in writing. They will also complete a term paper in the form of a research proposal directed to the goal of using bioinformatics to advance a medical procedure – either diagnostic or therapeutic. Brief guest presentations by researchers in BUSM laboratories will be arranged as appropriate.

Prereq: consent of the instructor and completion of advising form. Email Scott Mohr (mohr@bu.edu) or Katie Steiling (steiling@bu.edu) to enroll.

Instructor: Steiling; Credits: 2; Day/Time: Wed, 11:00 pm – 1:00 pm 

ENG BF 527: Applications in Bioinformatics

The field of bioinformatics is concerned with the management and analysis of large biological datasets (such as the human genome) for the purpose of improving our understanding of complex living systems. This course introduces graduate students and upper-level undergraduate students to the core problems in bioinformatics, along with the databases and tools that have been developed to study them. Computer labs emphasize the acquisition of practical bioinformatics skills for use in students research. Familiarity with basic molecular biology is a prerequisite; no prior programming knowledge is assumed. Specific topics will include the analysis of biological sequences, structures, and networks.

Instructor: Leyfer; Credits: 4; Mon/Wed 12:00 pm – 2:00 pm

ENG BE 700: Advanced Topics in Biomedical Engineering

Description: Advanced study of a specific research topic in biomedical engineering. Intended primarily for advanced graduate students.

Prereq: Graduate standing or consent of instructor.

Instructor: Mehta; Credits: 4; Mon/Wed 10:00 am – 12:00 pm.

CAS BI 527: Biochemistry Laboratory I

Emphasizes the purification and characterization of proteins and DNA. Development and use of modern instrumentation and techniques. Same as CH527 and laboratory portion of CAS BI/CH421. Four hours lab, one hour discussion.

Prereq: (CASCH204 & CASCH212 & CASCH214) or CASCH282

Instructor: Tolan; Credits: 4; Multiple meetings.  See University class schedule for complete list.

CAS BI 560: Systems Biology

Examines critical components of systems biology, including design principles of biological systems (e.g., feedback, synergy, cooperativity), and the generation and analysis of large-scale datasets (e.g., protein- protein interaction, mRNA expression).

Prereq: CAS BI 552 or consent of the instructor.

Instructor: Siggers; Credits: 4; LEC: Mon/Wed/Fri 11:00 am – 12:00 pm, DIS: Wed 12:00 pm – 1:00 pm

CAS BI 572: Advanced Genetics

An in-depth study of eukaryotic genetics, ranging from the history and basic principles to current topics and modern experimental approaches. Genetics of Drosophila, C. elegans, mice, and humans are explored in detail, including readings from primary literature. Three hours lecture, one hour discussion.

Prereq: CAS BI 206 and CAS BI 203; CAS BI 552 is recommended.

Instructor: McCall; Credits: 4; LEC: Tues/Thurs 9:30 am – 11:00 am, DIS: Wed 10:00 am – 11:00 am

CAS BI 610: Developmental Biology

Comtemporary aspects of development, drawing from current literature. Emphasis on the use of experimental approaches to address topics such as polarity in the egg, body axis specification, embryonic patterning and organogenesis. Three hours lecture, one hour discussion.

Instructor: Bradham; Credits: 4; LEC: Tues/Thurs 2:oo – 3:30 pm, DIS: Wed 2:00 – 3:00 pm, 3:00 – 4:00 pm.

GRS BI 753: Advanced Molecular Biology

In-depth analysis of current topics in molecular biology regarding the flow of information in the nucleus of eukaryotic cells. Focus on primary literature. Includes genomic flexibility, signal transduction to the nucleus, chromatin structure, gene expression, cell cycle checkpoints, health-related topics.

Prereq: CAS BI 552 or consent of instructor.

Instructor: Hansen; Credits: 4; LEC: Mon/Wed 2:00 pm – 3:30 pm, DIS: Wed 11:00 am – 12:00 pm

GRS BI 755: Cellular and Systems Neuroscience

Survey course in neurobiology. Topics covered include cell biology of the neuron, development of the nervous system, synaptic plasticity, learning and behavior, and network modeling. Three hours lecture, one hour discussion.

Instructor: Eldred; Credits: 4; Tues/Thurs 3:30 pm – 6:00 pm

CAS CS 565: Data Mining

Introduction to data mining concepts and techniques. Topics include association and correlation discovery, classification and clustering of large datasets, outlier detection. Emphasis on the algorithmic aspects as well as the application of mining in real-world problems.

Grad Prereq: CAS CS 112; or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.

Instructor: Terzi; Credits: 4; Mon/Wed 2:00 pm – 3:30 pm; LAB: Fri 10:00 am – 11:00 am or 11:00 am – 12:00 pm.

SPH BS 858: Statistical Genetics I

This course covers a variety of statistical applications to human genetic data, including collection and data management of genetic and family history information, and statistical techniques used to identify genes contributing to disease and quantitative traits in humans. Specific topics include basic population genetics, linkage analysis and genetic association analyses with related and unrelated individuals.

Grad Prereq: SPH BS723 or equivalent as determined by instructor.

Instructor: Lunetta; Credits: 4; Thurs 2:00 pm – 5:00 pm

CAS MA 575: Linear Models

Post-introductory course in linear models, with focus on both principles and practice. Simple and multiple linear regression, weighted and generalized least squares, polynomials and factors, transformations, regression diagnostics, variable selection, and a selection from topics on extensions of linear models.

Prereq: (CASMA214 & CASMA242 & CASMA581) or consent of instructor.

Instructor: Zhang; Credits: 4; Tues/Thurs 9:30 am – 11:00 am; DIS: Mon 9-10:00, Mon 1-2:00, Mon 3-4:00, or Mon 4:00-5:00

CAS MA 579: Numerical Methods for Biological Sciences

Introduction to the use of numerical methods for studying mathematical models of biological systems. Emphasis on the development of these methods; understanding their accuracy, performance, and stability; and their application to the study of biological systems.

Prereq: (CASMA226 OR CASMA231) or equivalent, and elementary knowledge of linear algebra.

Instructor: Isaacson; Credits: 4; Tues/Thurs 11:00 am – 12:30 pm

CAS MA 581: Probability

Basic probability, conditional probability, independence. Discrete and continuous random variables, mean and variance, functions of random variables, moment generating function. Jointly distributed random variables, conditional distributions, independent random variables. Methods of transformations, law of large numbers, central limit theorem.

Prereq: (CAS MA 225 OR CAS MA 230) or consent of instructor

Instructor: Taqqu; Credits: 4; LEC: Mon/Wed/Fri 11:00 am – 12:00 pm & DIS: either Mon 1-2:00, Mon 2-3:00, Tues 9:30-10:30 or Tues 4:30-5:30

GRS MA 614: Statistical Methods II

Second course in statistics, embodying basic statistical methods used in educational and social science research. Reviews all basic concepts covered in a first statistics course (e.g., CAS MA 613) and presents, in detail, more advanced topics such as analysis of variance, covariance, experimental design, correlation, regression, and selected nonparametric techniques. A problem-solving course; students carry out analysis of data taken from educational and other social science sources. This course cannot be taken for credit in addition to the course entitled “Statistical Methods II” that was previously numbered CAS MA 614.

Prereq: Graduate standing in education or in the social sciences.

Instructor: Heeren; Credits: 4; Mon 3:00 pm – 6:00 pm

GRS MA 684: Applied Multiple Regression and Multivariable Methods

Application of multivariate data analytic techniques. Multiple regression and correlation, confounding and interaction, variable selection, categorical predictors and outcomes, logistic regression, factor analysis, MANOVA, discriminant analysis, regression with longitudinal data, repeated measures, ANOVA. This course cannot be taken for credit in addition to the course with the same title that was previously numbered CAS MA 684.

Prereq: One year of statistics

Instructor: TBA; Credits: 4; Tues/Thurs, 11:00 am – 12:30 pm; DIS: Tues, 8:30-9:30 am

GRS MA 881: Seminar: Statistics

Advanced seminar in topics in statistics of current research interest.

Prereq: GRS MA782

Instructor: TBA; Credits: 4; Tues/Thurs 12:30 pm – 2:00 pm

GRS MB 721: Graduate Level Biochemistry

Introductory biochemistry course that in one semester covers the major principles of biochemistry; proteins, nucleic acids, carbohydrates, lipids, and metabolism. Emphasis on how knowledge was derived and the theoretical principles governing biochemistry.

Instructor: Kornberg; Credits: 4; Tues/Thurs 12:30 pm – 2:00 pm & Wed 4:00 pm – 6:00 pm

ENG EC 533: Advanced Discrete Mathematics

Selected topics in discrete mathematics. Formal systems. Mathematical deduction. Logical concepts. Theorem proving. Sets, relations on sets, operations on sets. Functions, graphs, mathematical structures, morphisms, algebraic structures, semigroups, quotient groups, finite-state machines, their homomorphism, and simulation. Machines as recognizers, regular sets. Kleene theorem.

Prereq: (CASMA124) or equivalent

Instructor: Levitin; Credits: 4; Mon/Wed 2:00 pm – 4:00 pm

Spring 2016

Core Courses

Description: This course consists of two modules:

(a) “Molecules” – an introduction to the molecular make-up of living organisms, inel dynamic behaviors, and

(b) “Processes” – a survey of selected biochemical and cellular functions viewed from a systems-biology perspective.

Instructor: Mohr; Credits: 4; Tues & Fri 10:00 am – 12:00 pm

Description: Project course for first year Bioinformatics graduate students. Open-ended problems will involve bioinformatics as a key element, typically requiring the use of large data sets and computational analysis to make predictions about molecular function, molecular interactions, regulation, etc. Projects will be proposed by the Bioinformatics program faculty and selected by student in teams of three. The end result will be a set of predictions, some of which can be validated retrospectively using data available through online sources and some of which will require experimental validation. During the last 2 months of the academic year, teams will design feasible validation experiments in consultation with the experimental faculty.  1st year Bioinformatics PhD students only.

Instructor: Monti; Credits: 2; Thurs 10:30 am – 12:00 pm

Description: The course focuses on mathematical models for exploring the organization, dynamics, and evolution of biochemical and genetic networks. Topics include: introductions to metabolic and genetic networks, deterministic and stochastic kinetics of biochemical pathways; genome-scale models of metabolic reaction fluxes; models of regulatory networks; modular architecture of biological networks. Prereq: CAS MA 226 & CAS MA 242. ENG EK 102 can be used in lieu of the CAS MA 242 pre-req. Familiarity with differential equations and linear algebra at equivalent levels and the consent of instructor can be used in lieu of both pre-reqs.

Instructors: Segre; Credits: 4; Mon/Wed 10:00 am – 12:00 pm

Description: This course will address the ethical, legal and scientific aspects of the new genetics. Students in bioinformatics will discuss the questions raised from another view that they normally would not see. As part of the new technologies, individuals, families and society as a whole will have to make decisions that will affect everyone.  We will analyze cases, question the legal system’s role in regulating this field, discuss options for now and analyze cases. Gene therapy, DNA forensics, new reproductive techniques and cloning are only a few of the topics that will be addressed.

Instructor: McGreevy ; Credits: 4; Date/time Mon 4:30 pm – 6:30 pm 

Description: Describes relational data models and database management systems. Teaches the theories and techniques of constructing relational databases with emphasis on those aspects needed for various biological data, including sequences, structures, genetic linkages and maps, and signal pathways. Introduces relational database query language SQL. Summarizes currently existing biological databases and the Web-based programming tools for their access. Object-oriented modeling is introduced primarily as a design aid for dealing with the particular complexities of biological information in standard RDB design. Emphasis will be on those problems associated with dealing with data whose nomenclature and interrelationships are undergoing rapid change.

Instructor: Benson; Credits 4; Tues/Thurs 3:00 pm – 6:00 pm

Description: Three laboratory rotations are required during a Bioinformatics Ph.D. student’s first year. Rotations typically last for a minimum of nine weeks. It is expected that the student will participate in the lab full time except for time spent on courses. One rotation must be experimental, one computational, and the third can be either. Stduents who participate in the Summer Wet-Lab Experience prior entering the program receive credit toward one of the required rotations.

Instructor: Varied; Credits: 1 per rotation (3 total); Day: To be arranged

Description: BF821 is a graduate seminar covering current topics in bioinformatics. This is achieved through the critical reading, presentation, and discussion of recent literature. Additionally, the course is intended to give students the opportunity to practice and improve their scientific presentation abilities. As such, peer feedback on presentations is an integral aspect of the course. Students will present twice during the semester so that they may improve upon their presentation skills based on peer comments.

Instructor: Dukovski; Credits: 2; Wed 1:30 pm – 3:30 pm

The field of bioinformatics is concerned with the management and analysis of large biological datasets (such as the human genome) for the purpose of improving our understanding of complex living systems. This course introduces graduate students and upper-level undergraduate students to the core problems in bioinformatics, along with the databases and tools that have been developed to study them. Computer labs emphasize the acquisition of practical bioinformatics skills for use in students research. Familiarity with basic molecular biology is a prerequisite; no prior programming knowledge is assumed. Specific topics will include the analysis of biological sequences, structures, and networks.

Instructor: Leyfer; Credits: 4; Mon/Thurs 12:00 pm – 2:00 pm

Description: Physical structure and properties of DNA. The physical principles of the major experimental methods to study DNA are explained, among them: X-ray analysis, NMR, optical methods (absorption, circular dichroism, fluorescence), centrifugation, gel electrophoresis, chemical and enzymatic probing. Different theoretical models of DNA are presented, among them the melting (helix-coil) model, the polyelectrolyte model, the elastic-rod model, and the topological model. Theoretical approaches to treat the models, (e.g., the Monte Carlo method) are covered. Special emphasis is placed on DNA topology and DNA unusual structures and their biological significance. Major structural features of RNA are considered in parallel with DNA. The main principles of DNA-protein interaction are presented. the role of DNA and RNA structure in most fundamental biological proceses, replication, transcription, recombination, reparation, and translation is considered.

Prereq: CAS PY 212 & CAS CH 102.

Instructors: Frank-Kamenetskii; Credits: 4; Tues/Thurs 4:00 pm – 6:00 pm; PSY.

Description: Advanced study of a specific research topic in biomedical engineering. Intended primarily for advanced graduate students.

Prereq: Graduate standing or consent of instructor.

Instructor: Chen; Credits: 4; Mon/Wed 10:00 pm – 12:00 pm. CAS.

Description: Emphasizes protein, carbohydrate, nucleic acid, and lipid chemistry. Development and use of modern instrumentation and techniques. Four hours lab, one hour discussion. Same as CAS CH528 and laboratory portion of CAS BI/CH422. Required for BMB students enrolled concurrently in GMS BI 556. Four hours lab, one hour discussion.

Prereq: CASCH204 & CASCH212 & CASCH214 or CASCH282.

Instructor: Kornberg; Credits: 2; Lecture: Fri 10:00 am – 11:00 am, Lab: Mon 10 am – 2 pm, Mon 3 pm – 7: pm, Tue 6 pm – 10 pm, Wed 1 pm – 5 pm, Wed 5:30 pm – 9:30 pm, or Thurs 6 pm – 10 pm

Description: Continuation of CAS BI 552 with emphasis on eukaryotes. General areas of focus include genome organization, mechanisms of gene regulation, and cell signaling. Topics including genomics, mouse transgenics systems, signal transduction, chromatin structure, and cell cycle.

Prereq: CAS BI 552; (CAS BI/CH 421/422 recommended.)

Instructor: Naya; Credits: 4; Tues/Thurs 6:00 pm – 8:00 pm, Dis. Mon 1:00-2:00 pm or 5:00-6:00pm

Description: Comtemporary aspects of development, drawing from current literature. Emphasis on the use of experimental approaches to address topics such as polarity in the egg, body axis specification, embryonic patterning and organogenesis. Three hours lecture, one hour discussion.

Instructor: Bradham, Credits: 4.0; Lecture: Tue & Thurs 2:00 to 3:30; Discussion: Wed 2:00-3:00 or Wed 3:00-4:00

Description: Current understanding of essential topics and important problems in modern cell biology, with emphasis on recent experimental findings, research strategies and approaches, and new techniques for investigating how cells work. Three hours lecture, one hour discussion.

Instructor: Mccall, Credits: 4.0; Lecture: Tue & Thurs 2:00-3:30; Discussion: Wed 1:00-2:30 pm

Description: Introduction to techniques of molecular biology research, including analysis of DNA, RNA, and protein molecules by techniques such as restriction enzyme digestions, PCR, subcloning, DNA sequencing and analysis, reporter gene assays, protein-protein interactions, and culturing and yeast molecular biology.

Prereq: (CASBI552)

Instructor: Spilios, Credits: 4.0; Lecture: Tues & Thurs 1:00-5:00 pm (Web Restricted Permission Required)

Description: Cannot be taken as advanced course for chemistry majors or in addition to CAS CH351/352. Introduction to physical chemical principles with topics in biochemistry, solution and solid phase chemistry of biomolecules as studied by equilibrium, hydrodynamics, and spectroscopic/quantum mechanical methods.

Prereq:(CASCH421 OR CASBI421); (CASMA121 OR CASMA123); (CASPY106 OR CASPY212).

Instructor: Elliot, Credits: 4.0; Independent: Mon, Wed & Fri 10:00 to 11:00 am

Description: Current topics of research in chemical physics. Content varies with the instructor but may include material from such areas as advanced methods in molecular spectroscopy and magnetic resonance, non-linear laser-induced phenomena, and photoionization and electron-molecule scattering. Topic for Spring 2015: Advanced Computational Chemistry.

Prereq: GRS CH 651

Instructor: Coker, Credits: 4.0; Lecture: Mon & Wed 9:30-11:00 am

Description: Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.

Prereq: (CASCS112) or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.

Instructor: Sclaroff, Credits: 4.0; Lecture: Tue & Thurs 2:00-3:30; Lab: Mon 10:00-11:00 or Mon 11:00-12:00

Description: This course covers current topics in statistical genetics, with emphasis on how statistical techniques can be used with various types of genetics data for mapping genes responsible/contributing to complex human diseases. Topics such as genetics map functions, gene mapping in experimental organisms, advanced linkage analysis methods, statistical approaches for the analysis of genome-wide high density SNP scans in unrelated and family samples will be discussed.

Pre-req: SPH BS858 or consent of instructor (dupuis@bu.edu)

Instructor: Dupuis, Credits: 4.0; Lecture: Tues 6:00-9:00 pm

Description: Numerical solutions of equations, iterative methods, analysis of sequences. Theory of interpolation and functional approximation, divided differences. Numerical differentiation and integration. Polynomial theory. Ordinary differential equations.

Prereq: (CASMA225 OR CASMA230) Grad Prereq:(CASMA225 OR CASMA230)

Instructor: Fried, Credits: 4.0; Lecture: Mon, Wed and Fri 3:00 to 4:00

Description: An introduction to mathematical modeling, using applications in the biological sciences. Mathematics includes linear difference and differential equations, and an introduction to nonlinear phenomena and qualitative methods. An elementary knowledge of differential equations and linear algebra is assumed.

Prereq: (CAS MA226 or CAS MA231)

Instructor: Vo, Credits: 4.0; Lecture: Tues & Thurs 11:00 am-12:30 pm

Description: Covers topics in linear models beyond MA 575: generalized linear models, analysis of binary and polytomous data, log-linear models, multivariate response models, non-linear models, graphical models, and relevant model selection techniques. Additional topics in modern regression as time allows.

Prereq: (CASMA575) or consent of instructor.

Instructor: Carvalho, Credits: 4.0; Lecture: Mon, Wed and Fri 1:00 to 2:00

Description: Basic probability, conditional probability, independence. Discrete and continuous random variables, mean and variance, functions of random variables, moment generating function. Jointly distributed random variables, conditional distributions, independent random variables. Methods of transformations, law of large numbers, central limit theorem. Cannot be taken for credit in addition to CAS MA 381.

Grad Prereq:(CAS MA225 or CAS MA230) or consent of instructor.

Instructor: Salins, Credits: 4.0; Lecture: Mon, Wed and Fri 10:00-11:00 am; Discussion: Mon 3:00-4:00 or Mon 4:00-5:00 or Tues 2:00-3:00 or Tues 1:00-2:00

Description: Point estimation including unbiasedness, efficiency, consistency, sufficiency, minimum variance unbiased estimator, Rao-Blackwell theorem, and Rao-Cramer inequality. Maximum likelihood and method of moment estimations; interval estimation; tests of hypothesis, uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, and chi-square test.

Prereq: (CASMA581 OR CASMA381)

Grad Prereq:(CASMA581)

Instructor: Weiner, Credits: 4.0; Lecture: Mon, Wed and Fri 11:00-12:00; Discussion: Tues 3:30-4:30 or Tues 4:30-5:30 or Wed 11:00-12:00 or Wed 12:00-1:00

Description: Basic concepts and techniques of stochastic process as they are most often used to construct models for a variety of problems of practical interest. Topics include Markov chains, Poisson process, birth and death processes, queuing theory, renewal processes, and reliability.

Prereq: (CASMA581 OR CASMA381) or consent of instructor.

Grad Prereq:(CASMA581) or consent of instructor.

Instructor: Spiliopoulos, Credits: 4.0; Lecture: Tues and Thurs 9:30-11 am; Discussion: Wed 3:00-4:00 or Tues 12:30-1:30 pm or Tues 1:30-2:30 pm

Description: Application of multivariate data analytic techniques. Multiple regression and correlation, confounding and interaction, variable selection, categorical predictors and outcomes, logistic regression, factor analysis, MANOVA, discriminant analysis, regression with longitudinal data, repeated measures, ANOVA.

Grad Prereq: one year of statistics.

Instructor: Heeren, Credits: 4.0; Lecture: Mon 4:00 to 7:00

Description: Mathematical and statistical bases of bioinformatics methods and their applications. Hidden Markov models, kernel methods, mathematics of machine learning approaches, probabilistic sequence alignment, Markov chain Monte Carlo and Gibbs sampling, mathematics of phylogenetic trees, and statistical methods in microarray analysis.

Prereq: Graduate standing or advanced undergraduate math/statistics major, (CAS MA225), (CAS MA242), and previous work in mathematical analysis and probability.

Instructor: TBA, Credits: 4.0; Lecture: Tues and Thurs 2:00-3:30 pm

Description: Course description is not currently available. Please contact the School or College offering the course.

Instructor: Tolan, Credits: 4.0; Lecture Tue & Thurs 12:30-2:00 pm; Discussion Wed 4:30-6:30

Description: Lectures and interactive auto-tutorial case studies presenting the basic morphologic and functional changes associated with cell injury and death, inflammation, response to microorganisms, atherosclerosis, cancer, and organ system pathology.

Prereq: GMS FC701, FC702, FC703 or GMS BI751 & PH730.

Instructor: Slack, Credits: 4.0; Lecture: Tues and Thurs 1:00-3:00 pm

Description: Recently developed information-theoretical approach to the analysis and design of computer algorithms. Previous knowledge of information theory or the theory of algorithms is not required, though desirable. Main topics include the complexity of algorithms; P, E, NP, and NP?hard problems; basic concepts of information theorfy, optimal coding; information-theoretical approach to sorting, order statistics, binary search, decision trees, hashing, minimization of Boolean functions, test, and similar problems; and design of efficient computer algorithms.

Instructor: Levitin, Credits: 4.0; Lecture: Mon & Wed 2:00-4:00