Fall 2015 Courses
CAS BI 552: Molecular Biology I
Description: 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. Required for Bioinformatics M.S. students.
Prereq: BI 203 or BI 213; BI 206 or BI 216.
Instructor: Loechler; Credits: 4; Tues/Thurs 3:30 pm – 5:30 pm
ENG BE 562: Computational Biology: Genomes, Networks, Evolution
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, 2 pm – 4:00 pm and Fri, 10 am – 11:30 am
GRS MA 681: Accelerated Introduction to Statistical Methods for Quantitative Research
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: Eden; Credits: 4; Tues/Thurs, 9:30 am – 11 am
ENG BF 820: Bioinformatics Research Opportunities
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, 2 pm – 4 pm
ENG BF 690: Bioinformatics Challenge Project
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
ENG BF 810: Bioinformatics PhD Laboratory Rotation
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.
ENG BF 821: Bioinformatics Graduate Seminar
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
ENG BF 831: Translational Bioinformatics Seminar
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 (firstname.lastname@example.org) or Katie Steiling (email@example.com) to enroll.
Instructor: Steiling; Credits: 2; Day/Time: TBA
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: TBA; Credits: 4; Mon/Wed 12:00 pm – 2:00 pm
ENG BE 560: Biomolecular Architecture
Provides an introduction to the molecular building blocks and the structure of three major components of the living cells: the nucleic acids, the phospho- lipids membrane, and the proteins. The nucleic acids, DNA and RNA, linear information storing structure as well as their three-dimensional structure are covered in relationship to their function. This includes an introduction to information and coding theory. The analysis tools used in pattern identification representation and functional association are introduced and used to discuss the patterns characteristic of DNA and protein structure and biochemical function. The problems and current approaches to predicting protein structure including those using homology, energy minimization, and modeling are introduced. The future implications of our expanding biomolecular knowledge and of rational drug design are also discussed.
Prereq: (CAS PY212 & CAS CH131) or CAS CH102
Instructor: Vajda; Credits: 4; Lec: Tues/Thurs 2:00 pm – 4:00 pm; Lab: Arranged
CAS PY 895: Methods and Logic in Quantitative Biology
Biology is in the midst of a transformation into a fully quantitative, theory-rich science. For example, the advent of genomic techniques has presented the opportunity to study genetic processes on a genomic scale and to achieve quantitative understanding, not just of individual molecular mechanisms but also of their interactions and regulation at the systems level. This graduate course in quantitative biology is based on original path-breaking papers in diverse areas of biology. Each of these papers depends on quantitative reasoning and theory as well as experiment. Through close reading and discussion of these papers, students of diverse backgrounds (biology, engineering, physics, computational sciences) will learn essential ideas and how to communicate in a common language of modern/quantitative biology. This course should be considered essential education for students interested in pursuing research in bioinformatics, biophysics, systems biology, and synthetic biology. Although it is a graduate course, we encourage advanced undergraduates to enroll, too.
Prereq: Graduate standing or consent of instructor.
Instructors: Mehta; Credits: 4; Days/Time: TBA
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
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/Fri 2:30 pm – 4:00 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
SPH BS 830: Design & Analysis of Microarray Experiments & Next Generation Sequencing
In this course, students will be presented with the methods for the analysis of gene expression data measured through microarrays. The course will start with a review of the basic biology of gene expression and an overview of microarray technology. The course will then describe the statistical techniques used to compare gene expression across different conditions and it will progress to describe the analysis of more complex experiments designed to identify genes with similar functions and to build models for molecular classification. The statistical techniques described in this course will include general methods for comparing population means, clustering, classification, simple graphical models and Bayesian networks. Methods for computational and biological validation will be discussed.
Grad Prereq: MPH biostatistics core course or BS723 required or consent of instructor (firstname.lastname@example.org). Recommended: Basic biology.
Instructor: Sebastiani; Credits: 4; Fri 2:00 pm – 4:45 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 – 4:45 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 & discussion either Mon 9-10:00, Mon 1-2:00, Mon 2-3:00 or Mon 3-4:00
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: Wright; Credits: 4; Mon/Wed/Fri 10:00 am – 11:00 am
GRS MA 703: Statistical Analysis of Network Data
Methods and models for the statistical analysis of network data, including network mapping and characterization, community detection, network sampling and measurement, and the modeling and inference of network and networked-indexed processes. Balance of theory and concepts, illustrated through various applications.
Prereq: CAS MA 575 or GRS MA 681 or consent of instructor
Instructor: Kolaczyk; Credits: 4; Mon/Wed/Fri 10:00 am – 11:00 am
GRS MA 881-B1: Seminar: Statistics
Topic for Section B1: Data Science with R. Introduction to both core elements of R and its use within the larger data science process. Topics include data manipulation, visualization, and statistical analysis, as well as workflow and reproducibility, integration with other data environments, and scaling to big data.
Prereq: GRS MA782
Instructor: TBA; Credits: 4; Mon/Wed/Fri 9:00 am – 10:00 am
GRS MB 721: Graduate Level Biochemistry
Course description is not currently available.
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
ENG BF 751: Molecular Biology and Biochemistry: Molecules and Processes
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
ENG BF 690: Bioinformatics Challenge Project
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: Benson; Credits: 2; Thurs 10:30 am – 12:30 pm
ENG BF 571: Dynamics and Evolution of Biological Networks
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 & Korolev; Credits: 4; Mon/Wed 10:00 am – 12:00 pm
ENG BF 752: Legal and Ethical Issues of Science and Technology
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: Yashon; Credits: 4; Mon 4:30 pm – 6:30 pm
ENG BF 768: Biological Database Systems
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 4:00 pm – 7:00 pm
ENG BF 810: Laboratory Rotation System
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
ENG BF 821: Bioinformatics Graduate Seminar
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: Korolev; Credits: 2; Wed 1:30 pm – 3:30 pm
ENG BF 591:BF Special Topics: Applications in Translational Bionformatics
Description: Bioinformatics is the science of managing and analyzing large-scale molecular biology data, such as the DNA sequence of the human genome, and has become an essential tool in interpreting and translating biological knowledge for use in a clinical setting. This course introduces graduate and upper–level undergraduate students to the principles of bioinformatic analysis applied to translational studies. Application topics will include gene-expression analysis, biomarker development, and Genome Wide Association Studies (GWAS). Bioinformatics methods including microarray analysis, short read sequence analysis, biological pathways and geneset enrichment analysis, and Quantitative Trait Loci (QTL) will be covered. Lectures and assignments will be designed around reproducing the results of preselected studies from the literature that exemplify the topics. The primary focus will be using existing software tools and published data to perform analyses, but most tasks will require some programming. The course does not assume advanced statistical knowledge or programming experience, but facility in these areas will be helpful. The goal of the course is to give students opportunity to gain familiarity with the bioinformatics tools and techniques currently employed in translational studies, and to facilitate better understanding of the computational aspects of published translational studies.
Instructors: Mohr, Labadorf, Pavel & Rogers; Credits: 4; Tues 2:00 pm – 5:00 pm; Offered at BU Medical Campus building L, room 209.
ENG BE 560: Biomolecular Architecture
Description: Provides an introduction to the molecular building blocks and the structure of three major components of the living cells: the nucleic acids, the phospho-lipids membrane, and the proteins. The nucleic acids, DNA and RNA, linear information storing structure as well as their three-dimensional structure are covered in relationship to their function. This includes an introduction to information and coding theory. The analysis tools used in pattern identification representation and functional association are introduced and used to discuss the patterns characteristic of DNA and protein structure and biochemical function. The problems and current approaches to predicting protein structure including those using homology, energy minimization, and modeling are introduced. The future implications of our expanding biomolecular knowledge and of rational drug design are also discussed.
Prereq: CAS PY 212 & CAS CH 131 or CAS CH 102.
Instructor: Vajda; Credits: 4; Tues/Thurs 2:00 pm – 4:00 pm
ENG BE 568: Systems Biology of Disease
Description: This course will train students to apply or develop new computational network and machine learning concepts to probe into the systems biology of disease.
The course will cover computational frameworks such as biological networks (including metabolic, regulatory and signal transduction networks), microarray analysis, proteomic analysis, next. generation sequencing, imaging, machine learning, genetics, pathway analysis and other technologies to medical diseases initially focusing on clinical problems such as cancer, diabetes, inflammation and aging.
The course is aimed at seniors and graduate students in biomedical engineering or bioinformatics. However, students from other disciplines ranging from medicine and biology to physics or computer science can attend the class with some prerequisites. .
Instructor: Kasif; Credits: 4; Mon/Weds 6:00 pm – 8:00 pm
ENG BF 527: Applications in Bioinformatics
Description: 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 student 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.
Prereq: Familiarity with basic molecular biology.
Instructor: Speranza & Loving; Credits: 4 Mon/Wed 12:00 pm – 2:00 pm
CAS BI/CH 528: Biochemistry Laboratory 2
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
CAS BI 504: Advanced Evolutionary Analysis
Description: Modern concepts, controversies, and analytical approaches in evolutionary biology. Topics include adaptation, natural and sexual selection, species and species formation, phylogenetics, origin of evolutionary novelty, adaptive radiation, basic population and quantitative genetics, development and evolution. Three hours lecture, one hour discussion.
Prereq: CAS BI309 or consent of instructor.
Instructor: Mullen, Credits: 4.0; Lecture: Tue & Thurs 11:00-12:30; Discussion: Wed 2:00-3:00
CAS BI 553: Molecular Biology II
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: (CASBI552) (CAS BI/CH 421/422 recommended.)
Instructor: Naya, Credits: 4.0; Lecture: Tue & Thurs 9:30 to 11:00; Discussion: Mon 1:00-2:00 or Mon 5:00-6:00
GRS BI610:Developmental Biology
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
CAS BB 522: Molecular Biology Laboratory
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.
Instructor: Spilios, Credits: 4.0; Lecture: Tues & Thurs 1:00-5:00 (Web Restricted Permission Required)
CAS CH 525: Physical Biochemistry
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
CAS CS 542: Machine Learning
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
CAS CS 591-T1: CS Special Topics: Tools & Techniques for Data Mining & Applications
Description: The course emphasizes practical skills in working with data, while introducing students to a wide range of techniques that are commonly used in the analysis of data, such as clustering, classification, regression, and network analysis. The goal of the class is to provide to students a hands-on understanding of classical data analysis techniques and to develop proficiency in applying these techniques in a modern programming language (Python).
Lectures will present the fundamentals of each technique; focus is not on the theoretical underpinnings of the methods, but rather on helping students understand the practical settings in which these methods are useful. Class discussion will study use cases and will go over relevant Python packages that will enable the students to perform hands-on experiments with their data.
Note this class is different from CS 565 (Data Mining): while CS 565 focuses on the fundamental algorithmic problems around a set of data-mining problems and emphasizes on the analysis of the algorithms for certain data analysis tasks, this class will focus on how these algorithms work in practice.
Prereq: Students taking this class must have some prior familiarity with programming, at the level of CS 105, 108, or 111, or equivalent. CS 112 is also helpful.
Instructor: Terzi, Credits: 4.0; Lecture: Mon & Wed 4:00-5:30;
SPH BS 859: Applied Genetic Analysis
Description: Statistical tools such as linkage and association analysis are used to unravel the genetic component of complex disease. Investigators interested in the genetic analysis of complex traits need a basic understanding of the strengths and weaknesses of these methodologies. This course will provide the student with practical, applied experience in performing linkage and association analyses, including genome-wide analyses. Special emphasis is placed on understanding assumptions and issues related to statistical methodologies for genetic analysis to identify genes influencing complex traits. Students will use specialized genetics software for homework assignments.
Pre-req: SPH BS858 or EP763 or consent of instructor.
Instructor: Lunetta, Credits: 4.0; Lecture: Tues 6:00-8:45 pm
CAS MA 555: Numerical Analysis I
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
CAS MA 576: Generalized Linear Models
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
CAS MA 579: Numerical Methods for Biological Sciences
Description: 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: (CAS MA226 or CAS MA231) or equivalent, and elementary knowledge of linear algebra.
Instructor: TBA, Credits: 4.0; Lecture: Mon, Wed and Fri 11:00 to 12:00
CAS MA 581: Probability
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: Spiliopoulos, Credits: 4.0; Lecture: Mon, Wed and Fri 9:00-10:00; Discussion: Mon 3:00-4:00 or Mon 4:00-5:00
CAS MA 582: Mathematical Statistics
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)
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 Thurs 9:30-10:30
CAS MA 583: Introduction to Stochastic Processes
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 Mon, Wed and Fri 10:00-11:00; Discussion: Wed 3:00-4:00 or Wed 4:00-5:00
GRS MA 684: Applied Multiple Regression and Multivariable Methods
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
GRS MA 770: Mathematical and Statistical Methods of Bioinformatics
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: Kon, Credits: 4.0; Lecture: Tues and Thurs 12:30-2:00
GRS MA 882: Statistics Seminar II
Description: Real problems in experimental design and data analysis presented by clients from various other departments. The art of statistical consulting in a variety of applied areas.
Prereq: GRS MA782
Instructor: Huang, Credits: 4.0; Lecture: Tues and Thurs 9:30-11:00
GRS MB 722: Advanced Biochemistry
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
ENG EC 730: Information –Theoretical Design of Algorithms
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