| CMU 03-533 - NMR in Biomedical Sciences |
| CMU 03-534 - Biological Imaging and Fluorescence Spectroscopy |
| This course covers principles and applications of optical methods in the study of structure and function in biological systems. Topics to be covered include: absorption and fluorescence spectroscopy; interaction of light with biological molecules, cells, and systems; design of fluorescent probes and optical biosensor molecules; genetically expressible optical probes; photochemistry; optics and image formation; transmitted-light and fluorescence microscope systems; laser-based systems; scanning microscopes; electronic detectors and cameras: image processing; multi-mode imaging systems; microscopy of living cells; and the optical detection of membrane potential, molecular assembly, transcription, enzyme activity, and the action of molecular motors. This course is particularly aimed at students in science and engineering interested in gaining in-depth knowledge of modern light microscopy. |
| CMU 03-711 - Computational Molecular Biology and Genomics |
| CMU 03-712 - Computational Methods for Biological Modeling and Simulation |
| This course covers a variety of computational methods important for modeling and simulation of biological systems. It is intended for graduates and advanced undergraduates with either biological or computational backgrounds who are interested in developing computer models and simulations of biological systems. The course will emphasize practical algorithms and algorithm design methods drawn from various disciplines of computer science and applied mathematics that are useful in biological applications. The general topics covered will be models for optimization problems, simulation and sampling, and parameter tuning. Course work will include problems sets with significant programming components and independent or group final projects.
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| CMU 03-730 - Advanced Genetics |
| This course considers selected current topics in genetics at an advanced level. The emphasis is on classroom discussion of research papers, supplemented with individual and group exercises. Topics change yearly. Recent topics have included genome imprinting in mammals, chromatin boundaries and long distance gene regulation, learning and memory in Drosophila,and the kinetochore complex in yeast. Must obtain a minimum grade of B in 03-330 to take this course.
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| CMU 03-740 - Advanced Biochemistry |
| This is a special topics course in which selected topics in biochemistry will be analyzed in depth with emphasis on class discussion of papers from the recent research literature. Topics change yearly. Recent topics have included single molecule analysis of catalysis and conformational changes; intrinsically disordered proteins; cooperative interactions of aspartate transcarbamoylase; and the mechanism of ribosomal protein synthesis.
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| CMU 03-741 - Advanced Cell Biology |
| This course covers fourteen topics in which significant recent advances or controversies have been reported. For each topic there is a background lecture by the instructor, student presentations of the relevant primary research articles and a general class discussion. Example topics are: extracellular matrix control of normal and cancer cell cycles, force generating mechanisms in trans-membrane protein translocation, signal transduction control of cell motility, and a molecular mechanism for membrane fusion.
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| CMU 03-742 - Molecular Biology of Eukaryotes |
| This course covers fourteen topics in which significant recent advances or controversies have been reported. For each topic there is a background lecture by the instructor, student presentations of the relevant primary research articles and a general class discussion. Example topics are: extracellular matrix control of normal and cancer cell cycles, force generating mechanisms in trans-membrane protein translocation, signal transduction control of cell motility, and a molecular mechanism for membrane fusion.
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| CMU 03-761 - Neural Plasticity in Sensory and Motor Systems |
| Neural plasticity underlies the capacity of the central nervous system to encode new information, develop new abilities and adapt to the environment. Plasticity is required for learning and is modulated during development and by disorders of the brain. Recent advances in experimental methodology have led to new insights on the biological mechanisms underlying neural plasticity. The topics if the papers chosen for review will center on recent experimental and theoretical studies of topics such as synaptic plasticity, developmental and activity dependent changes in sensory and motor maps.
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| CMU 09-560 - Molecular Biology |
| Computer modeling is playing an increasingly important role in chemical, biological and materials research. This course provides an overview of computational chemistry techniques including molecular mechanics, molecular dynamics, electronic structure theory and continuum medium approaches. Sufficient theoretical background is provided for students to understand the uses and limitations of each technique. An integral part of the course is hands on experience with state-of-the-art computational chemistry tools running on graphics workstations. 3 hrs. lec.
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| CMU 10-701 - Machine Learning |
| It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statististics and from statistical algorithmics. Students entering the class should have a pre-existing working knowledge of probability, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate.
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| CMU 10-702 - Statistical Machine Learning |
| This course builds on the material presented in 10-701(Machine Learning), introducing new learning methods and going more deeply into their statistical foundations and computational aspects. Applications and case studies from statistics and computing are used to illustrate each topic. Aspects of implementation and practice are also treated. A tentative list of topics to be covered includes (but is not restricted to) the following: Maximum likelihood vs. Bayesian inference; Regression, Classficiation, and Clustering; Graphical Methods, including Causal Inference; The EM Algorithm; Data Augmentation, Gibbs, and Markov Chain Monte Carlo Algorithms; Techniques for Supervised and Unsupervised Learning; Sequential Decision making and Experimental Design.
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| CMU 10-708 - Probabilistic Graphical Models |
| Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The general graphical models framework provides an unified view for this wide range of problems, enabling efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models. The class will cover three aspects: The core representation, including Bayesian and Markov networks, dynamic Bayesian networks, and relational models; probabilistic inference algorithms, both exact and approximate; and, learning methods for both the parameters and the structure of graphical models. Students entering the class should have a pre-existing working knowledge of probability, statistics, and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. Students are required to have successfully completed 10701/15781, or an equivalent class.
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| CMU 15-685 - Computer Vision |
| This course deals with the science and engineering of computer vision, that is, the analysis of patterns in visual images of the world with the goal of reconstructing and understanding the objects and processes in the world that are producing them. The emphasis is on physical, mathematical, and information processing aspects of vision, but biological and psychological perspectives will also be considered. Topics covered include image formation and representation, multi-scale analysis, segmentation, contour and region analysis, reconstruction of depth based on stereo, texture shading and motion, and analysis and recognition of objects and scenes using statistical and model-based techniques.
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| CMU 15-750 - Graduate Algorithms |
| CMU 15-782 - Artificial Neural Networks |
| Artificial neural networks combine ideas from machine learning, statistics, and pattern recognition. They draw inspiration from, and provide simplified formalizations of, theories about the workings of the brain. This course offers an introduction to neural networks for computer scientists and engineers. Prerequisites are undergraduate calculus and linear algebra, and solid programming skills. An undergraduate course in artificial intelligence or machine learning would provide helpful background but is not required. The course provides hands-on experience with a variety of neural network architectures implemented in MATLAB, and an in-depth look at problems in pattern recognition and knowledge representation.
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| CMU 15-785 - Computational Perception and Scene Analysis |
| CMU 15-853 - Algorithms in the Real World |
| CMU 15-859G - Computational Geometry |
| CMU 15-862 - Computational Photography |
| Computational Photography is an emerging new field created by the convergence of computer graphics, computer vision and photography. Its role is to overcome the limitations of the traditional camera by using computational techniques to produce a richer, more vivid, perhaps more perceptually meaningful representation of our visual world. The aim of this advanced undergraduate course is to study ways in which samples from the real world (images and video) can be used to generate compelling computer graphics imagery. We will learn how to acquire, represent, and render scenes from digitized photographs. Several popular image-based algorithms will be presented, with an emphasis on using these techniques to build practical systems. This hands-on emphasis will be reflected in the programming assignments, in which students will have the opportunity to acquire their own images of indoor and outdoor scenes and develop the image analysis and synthesis tools needed to render and view the scenes on the computer.
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| CMU 15-872A - Special Topics in Computational Biolgy: Formal Methods in Biology |
| CMU 15-873A - Advanced Topics in Computational Structural Biology |
| CMU 15-874 - Special Topics: Compuatational Neuroscience |
| CMU 15-879A - Algorithms for Computational Structural Biology |
| Some of the most interesting and difficult challenges in computational biology and bioinformatics arise from the determination, manipulation, or exploitation of protein structures. This course will survey these challenges and present a variety of computational methods for addressing them. The course is appropriate for both students with backgrounds in computer science and those in the life sciences.
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| CMU 15-883 - Computational Models of Neural Systems |
| CMU 16-725 - Medical Image Analysis |
| The fundamentals of computational medical image analysis will be explored, leading to current research in applying geometry and statistics to segmentation, registration, visualization, and image understanding. Student will develop practical experience through projects using the National Library of Medicine Insight Toolkit (ITK), a new software library developed by a consortium of institutions including CMU. In addition to image analysis, the course will describe the major medical imaging modalities and include interaction with practicing radiologists at UPMC.
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| CMU 18-798 - Image, Video and Multimedia |
| The course studies image processing, image understanding, and video sequence analysis. Image processing deals with deterministic and stochastic image digitization, enhancement. restoration, and reconstruction. This includes image representation, image sampling, image quantization, image transforms (e.g., DFT, DCT, Karhunen-Loeve), stochastic image models (Gauss fields, Markov random fields, AR, ARMA) and histogram modeling. Image understanding covers image multiresolution, edge detection, shape analysis, texture analysis, and recognition. This includes pyramids, wavelets, 2D shape description through contour primitives, and deformable templates (e.g., 'snakes'). Video processing concentrates on motion analysis. This includes the motion estimation methods, e.g., optical flow and block-based methods, and motion segmentation. The course emphasizes experimenting with the application of algorithms to real images and video. Students are encouraged to apply the algorithms presented to problems in a variety of application areas, e.g., synthetic aperture radar images, medical images, entertainment video image, and video compression.
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| CMU 21-660 - Introduction to Numerical Analysis I |
| Finite precision arithmetic, interpolation, spline approximation, numerical integration, numerical solution of linear and nonlinear systems of equations, optimization in finite dimensional spaces.
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| CMU 21-661 - Numerical Solution of Partial Defferential Equations I |
| CMU 21-662 - Numerical Solution of Partial Differential Equations II |
| CMU 21-665 - Introduction to Mathematical Modeling of Biological Systems |
| CMU 21-732 - Partial Differential Equations I |
| An introduction to the modern theory of partial differential equations. Including functional analytic techniques. Topics vary slightly from year to year, but generally include existence, uniqueness and regularity for linear elliptic boundary value problems and an introduction to the theory of evolution equations.
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| CMU 33-765 - Statistical Mechanics |
| This course develops the methods of statistical mechanics and uses them to calculate observable properties of systems in thermodynamic equilibrium. Topics treated include the principles of classical thermodynamics, canonical and grand canonical ensembles for classical and quantum mechanical systems, partition functions and statistical thermodynamics, fluctuations, ideal gases of quanta, atoms and polyatomic molecules, degeneracy of Fermi and Bose gases, chemical equilibrium, ideal paramagnetics and introduction to simple interacting systems. 3 hrs. lecture, 1 hr. recitation. Typical Texts: Reif, Statistical and Thermal Physics; Pathria, Statistical Mechanics.
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| CMU 36-625 - Probability and Mathematical Statistics I |
| This course is a fast-paced, rigorous introduction to the mathematical theory of probability, and statistical inference. It is ideal for students who want a crash-course in probability and mathematical statistics. A good working knowledge of calculus and basic linear algebra is required. Topics include sample spaces, probability, conditional probability, generating functions, sampling distributions, law of large numbers, the central limit theorem, maximum likelihood, the bootstrap, hypothesis testing, Bayesian inference, decision theory. Students studying Computer Science, or considering graduate work in Statistics or Operations Research, should carefully consider taking this course instead of 36-225 after consultation with their advisor. Not open to students who have received credit for 36-217 or 36-225.
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| CMU 36-705 - Intermediate Statistics |
| CMU 36-746 - Statistical Methods for Neuroscience |
| CMU 36-753/754 - Probability Theory and Stochastic Process |
| CMU 42-502 - Cellular Biomechanics |
| This course discusses how mechanical quantities and processes such as force, motion, and deformation influence cell behavior and function, with a focus on the connection between mechanics and biochemistry. Specific topics include: (1) the role of stresses in the cytoskeleton dynamics as related to cell growth, spreading, motility, and adhesion; (2) the generation of force and motion by moot molecules; (3) stretch-activated ion channels; (4) protein and DNA deformation; (5) mechanochemical coupling in signal transduction; (6) protein trafficking and secretion; and (7) the effects of mechanical forces on gene expression. Emphasis is placed on the biomechanics issues at the cellular and molecular levels; their clinical and engineering implications are elucidated.
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| CMU 42-708 - Special Topics: Registration Problems in Bioimaging |
| This course will cover the fundamentals of image matching (registration) methods with applications to biomedical engineering. As the fundamental step in image data fusion, registration methods have found wide ranging applications in biomedical engineering, as well as other engineering areas, and have become a major topic in image processing research. Specific topics to be covered include manual and automatic landmark-based, intensity-based, rigid, and nonrigid registration methods. Applications to be covered include multi-modal image data fusion, artifact (motion and distortion) correction and estimation, atlas-based segmentation, and computational anatomy. Course work will include Matlab programming exercises, reading of scientific papers, and independent projects. Upon successful completion, the student will be able to develop his/ her own solution to an image processing problem that involves registration. Prerequisites: 18-396 Signals and Systems or permission of the instructor, working knowledge of Matlab, and some image processing experience.
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| Pitt BIOE 2600/CMU 42-709 - Neuroimaging |
| This course consists of six state-of-the-art imaging techniques (i.e., MRI, MRS, fMRI, PET, MEG/EEG and Optical). Each part of the module will present indepth analysis of the each technique and its application in neuroscience research. Apart from in-depth presentation of the each technique, students will also get acquainted with the operation of the respective instruments. Tour to that respective facility will be guided by the concerned faculty and scientific staff member in that respective facility will assist for demonstration. This course is a joint program between PITT and CMU.
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| CMU 42-731 - Advanced Bioimaging |
| The goals of this course are to provide students with the following: the ability to use mathematical techniques such as linear algebra. Fourier theory and sampling in more advanced signal processing settings; fundamentals of multiresolution and wavelet techniques; and in-depth coverage of some bioimaging applications such as compression and denoising. Upon successful completion of this course, the student will be able to: explain the importance and use of signal representations in building more sophisticated signal processing tools, such as wavelets; think in basic time-frequency terms; describe how Fourier theory fits in a bigger picture of signal representations; use basic multirate building blocks, such as a two-channel filter bank; characterize the discrete wavelet transform and its variations; construct a time-frequency decomposition to fit a given signal; explain how these tools are used in various applications; and apply these concepts to solve a practical bioimaging problem through an independent project.
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| CMU 42-735 - Medical Image Analysis |
| The fundamentals of computational medical image analysis will be explored, leading to current research in applying geometry and statistics to segmentation, registration, visualization, and image understanding. Student will develop practical experience through projects using the National Library of Medicine Insight Toolkit (ITK), a new software library developed by a consortium of institutions including CMU. In addition to image analysis, the course will describe the major medical imaging modalities and include interaction with practicing radiologists at UPMC. Prerequisites: Permission of the instructor, knowledge of C++, vector calculus and basic probability.
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| CMU 85-719 - Introduction to Parallel Distributed Processing |
| This course will provide an overview of parallel-distributed processing models of aspects of perception, memory, language, knowledge representa-tion, and learning. The course will consist of lectures describing the theory behind the models as well as their implementation, and students will get hands-on experience running existing simulation models on workstations.
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| Pitt BIONF 2054 - Statistical Foundations for Bioinformatics Data Mining |
| This course provides an intermediate-level understanding of statistical foundations to prepare students for the competent use of data analysis methods in common practice in bioinformatics. Statistical ideas covered include probability distributions, likelihood theory, Bayesian and frequentist concepts, estimation, hypothesis testing and significance testing, multiplicity adjustments, the EM and MCMC algorithms, random walks, Poisson processes and Markov chains. Application areas include biological swquence analysis and microarray analysis. Students will learn the R statistical language. The R packages Bioconductor and BRB array tools for microarray analysis will be studied.
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| Pitt BIONF 2055 - Practical Analysis of High-Throughput Genomic & Proteomic Data |
| This course provides an in-depth comparative study of methods for the analysis and interpretation of high-throughput genomic and proteomic data sources. Using a broad survey of literature, the student will learn approaches to normalization/transformation, finding predictive biomarkers, methods for classification, cross-validation, functional interpretation. Ways to integrate diverse data sources are explored, including clinical outcomes. Lectures, exercises in use of publicly-available software, and intensive experience in analysis/interpretation of published data sets are included.
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| Pitt BIOINF 2101 - Probabilistic Methods for Computer Based Decision Support |
| This seminar provides an introduction to computational approaches for probabilistic modeling and inference. A particular focus is placed on Bayesian networks, although other probabilistic models also will be studied. Medical applications are emphasized, however, the principles are general and no medical knowledge is needed to take the course. The course does not require knowledge of a computer programming language.
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| Pitt BIOSC 2100 - Mechanisms of Cellular Communication, Structure and Morphology |
| This course will survey and discuss current literature pertaining to advances in understanding how cells regulate complex behaviors such as migration, cell polarity, protein/membrane trafficking, cell and tissue morphology, and cell proliferation/survival and how lesions in these processes result in human disease
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| Pitt BIOSC 2810 - Macromolecular Structure and Function |
| Course is concerned primarily with the structure and functions of proteins and nucleic acids. These are large polymers where structure and function are determined by the sequence of monomeric units. Topics will include the physical and chemical properties of the monomer units (amino acids/nucleotides); the determination of the linear sequence of these units; the size, shape and general properties of the biopolymers in aqueous systems; and the relation between structure and function, particularly in transport (hemoglobin) and in catalysis (enzymes).
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| Pitt BIOST 2015 - Elements of Statistical Learning |
| The purpose of the course is to present in thorough fashion the material in an outstanding book, Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman. This rigorous and clearly written book places "statistical learning" or "data mining" techniques in the proper context with regard to their origins in simple classical methosds like linear regression, to clarify the strengths and weaknesses from theoretical and practical sides. "Supervised learning" techniques studied incude using regularization and Bayesian methods, kernel methods, basis function methods, neural networks, support vector machines, additive trees, boosting bootstrap-based methods. Unsupervised learning techniques studied include cluster analysis, self-organizing maps, independent component analysis and projection pursuit.
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| Pitt BIOST 2043 - Intro to Statistical Theory |
| Covers joint, marginal, and conditional probabilities; distributions of random variables and functions of random variables; expectations of random variables and moment generating functions; law of large numbers; central limit theorem.
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| Pitt BIOST 2063 - Bayesian and Empirical Bayes Statistical Methods |
| The theoretical foundations of Bayesian and empirical Bayes statistical methods will be presented. The use of these methods in data analysis will be illustrated with specific examples and with discussions of common data analysis issues contrasts and similarities between Bayesian, empirical Bayesian, and classical methods will be evaluated.
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| Pitt BIOST 2064 - Bayesian and Empirical Bayes Computational Methods |
| Develop theory and practice for the EM algorithm. Markov chain sampling techniques, importance sampling, and other advanced ideas in statistical computation. Introduces computing on UNIX workstations with X Window and S-PLUS.
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| Pitt BIOST 2070 - Statistical Methods and Data Mining in Microarray Analysis |
| Introduces the student to specialized topics that are not covered in the formal curriculum.
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| Pitt CHEM 2430 - Quantum Mechanics and Kinetics |
| Basic quantum mechanics, with emphasis on the theory of chemical structure and dynamics.
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| Pitt CHEM 2440 - Thermodynamcis & Statistical Mechanics |
| Development of equilibrium statistical mechanics and thermodynamics. Applications to chemical systems. These applications include solutions, phase transitions (Ising model) and reaction theory.
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| Pitt CHE 2754 - Principles of Polymer Engineering |
| This course deals with the elements of polymer science and engineering necessary for entry-level understanding of polymer technology. While the chemistry determines macromolecular microstructure, an understanding of polymer manufacture and processing requires the addition of physical chemistry and transport phenomena. The essential material covered in this class includes the elements of polymers thermodynamics, rheology, mechanical behavior and equipment design.
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| Pitt CHEM 3430 - Introduction to Modern Computational Chemistry |
| This advanced quantum mechanics course will cover the treatment of angular momentum in electronic stages, scattering and spectroscopy. The use of Clebsch-Gordon and Wigner coefficients to analyze angular momentum coupling and recoupling will be discussed, as will be methods to create and measure polization and alignment in molecules.
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| Pitt CHEM 3450 - Molecular Modeling and Graphics |
| This course will introduce the student to computational methods to determine molecular structures and stabilities., Monte Carlo and molecular dynamics simulation methods, and the use of graphics for displaying structures, charge densities, and other properties. Use will be made of both microcomputers and the Cray C-90 at the Pittsburgh Supercomputing Center.
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| Pitt CS 2450 - Parallel Computing |
| The fundamental pricniples of parallel computing systems are studied at the architecture level, the algorithmic level and the application level. Topics of specific interest include: interconnection networks for multiprocessors and massively parallel systems, a survey of prototype and commercially available parallel machines, cache coherence issues, parallel programming constructs and paradigms, design and analysis of parallel algorithms, load balancing techniques, and the application of parallel systems to solve real problems.
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| Pitt CS 2550 - Principles of Database Design |
| The main objective of this course is to provide an in-depth knowledge of database management systems design. Topics covered at length are concurrency control including concurrency on structured data, recovery and query optimization. Some important aspects of distributed databases are discussed, including distributed concurrency control and fault tolerance.
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| Pitt MATH 2030 - Iterative Methods for Linear and Nonlinear Systems |
| The course gives an introduction to the iterative algorithms for solving linear and nonlinear systems. The course will cover the development and analysis of these numerical algorithms, to be used in the resolution of linear and nonlinear systems.
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| Pitt MATH 2070 - Numerical Methods in Scientific Computing |
| This is an introductory survey course for non-numerical analysis students. It covers the underlying theory and computational aspects of numerical linear algebra. Topics include directional iterative methods, computation of eigenvalues and eigenvectors and least squares problems.
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| Pitt MATH 2090 - Numerical Solution of Ordinary Differential Equations |
| This course aims to give an in-depth introduction to the numerical methods for solving ordinary differential equations. Both initial value problems and boundary value problems are considered. Important practical issues such as stability, stiffness, error estimation and control will be considered for Runge-Kutta methods, multistep methods and finite difference methods. If time permits, numerical techniques for differential-algebraic equations will be also presented.
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| Pitt MATH 3370 - Computational Models in Neuroscience |
| Course covers computational and mathematical neuroscience. Class will do modeling and analysis of complex dynamics of single neurons and large-scale networks.
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| Pitt MATH 3375 - Computational Neuroscience Methods |
| This course offers an introduction to modeling methods in neuroscience. Topics range from modeling the firing patterns of single neurons to using computational methods to understand neural coding. Some systems level modeling is also done.
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| Pitt MATH 3380 - Computational Cell Biology |
| This course introduces a number of modeling methods for biological systems. We will examine a number of problems from cell biology, immunology, population biology, physiology and molecular genetics. The main tools will be techniques from ordinary and partial differential equations. Discrete and delay-differential equations will also be used however the background for these will not be assumed. We will take models from current and classic papers in the field.
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| Pitt MSCBIO 2020 - Bioinformatics of Gene Regulation |
| This is a graduate level course designed primarily for students who want to learn about the computational methods and tools that are used in the analysis of promoter regions and transcription regulation data. Students with a biological background and knowledge of introductory level statistics can participate as well as students of quantitative background. The course will primarily focus on the methods that are used in the identification of transcription factor binding sites in the promoter regions of the genes. Both sequence-based and structure-based methods will be discussed. Various technologies for data collection will also be presented, including DNA arrays, SELEX, ChIP, and their derivatives.
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| Pitt MSCBIO 2035 - Computational Structural Biology II |
| The aim of computational structural biology is to understand the function of biological macromolecules such as proteins and nucleic acids in terms of fundamental physical forces. An important goal, for example, is to predict the 3d structure of proteins based on sequence data, to predict the structural organization of protein-protein and protein-DNA clusters, and to study the role of dynamics in molecular function (e.g., in enzymatic reactions). These studies are important in rational drug design. A great deal of progress has been made in the last 30 years, where sophisticated models and techniques have been developed consisting of quantum and statistical mechanics, simulation theory, electrostatics, etc. However, the challenges are still enormous. The objective of this course is to provide the student with a deeper understanding of the above disciplines as related to biological systems, which will enable him/her to participate in a state-of-the-art research. The course will cover some topics in irreversible thermodynamics (e.g., the fluctuation-dissipation theorem, Onsager relations), complementary material in statistical mechanics (phase transitions), simulation theory as applied to polymer chains and proteins (Rosenbluth, dimerization, scanning, multicanonical and its derivatives), free energy and potential of mean force (e.g., Jarzynki), continuum electrostatics (LaPlace, Poisson, and Poisson-Boltzmann equations, Born formula, generalized Born treatments), kinetics, and quantum mechanics. Most of the material will be presented in lectures by the course instructor, with the balance being presented by expert guest lecturers.
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| Pitt MSCMP 3780 - Systems Approach to Inflammation |
| This course is focused on particular topics of great biologic complexity in critical illness, where modeling has the potential to translate in improved patient care. Lectures are provided by basic (biological and mathematical sciences) and clinical faculty, in conjunction with members of industry and speakers from outside institutions. This information will be communicated within the framework of defined themes that describe the complexity of inflammation in acute and chronic illnesses.
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| Pitt MSMBPH 2001 - Molecular Biophysics I |
| This is the first of three courses, which together constitute the common core of the first year of the molecular biophysics graduate program. Here the emphasis is on the structural foundations, especially that of proteins and nucleic acids. Fundamental results are covered together with experimental techniques (X-ray, diffraction, NMR, EM/cryoEM, AFM, CD/ORD, Raman and fluorescence spectroscopy), as well as structural systematics and informatics.
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| Pitt MSMPHL 2310 - Principles of Pharmacology |
| This course consists of a series of lectures and tutorial sessions which focus on the general principles of pharmacology. Major topics are principles of pharmacokinetics (including drug absorption, distribution, and metabolism) and pharmacodynamics (quantitation of drug-receptor interactions).
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| Pitt MSMPHL 2370 - Drug Discovery
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| Pitt MSMPHL 3315 - Bioinformatics of Cancer Biology & Therapeutics |
| Reading and discussion on bioinformatics resources available to enhance research on cancer biology and therapeutics. We will discuss bioinformatics databases and other resources related to: regulatory networks and signal transduction pathways, genes associated with cancer risk and the progression of cancer; cytogenomics, sources of information on the distribution of cancer occurence and trends in the us population, databases DNA repair genes, their structure & function, models of cancer progression & responses to therapy, biomarkers for cancer detection, treatment & prevention.
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| Pitt MSMPHL 3360 - Molecular Pharmacology |
| This course examines molecular mechanisms of drug interactions with an emphasis on drugs that modulate cell signaling, cellular responses to drugs. The course will include student participation through presentations and discussion of relevant contemporary scientific literature. Topics include: cell cycle checkpoints and anti-cancer drugs, therapeutic control of ion channels, and blood glucose, anti-inflammatory agents and nuclear receptor signaling.
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| Pitt MSMPHL 3375 - Neuropharmacology |
| This course will examine the molecular mechanism of drug action for different classes of drugs that act on the nervous system, antidepressants, antipsychotics, drugs to relieve pain, drugs for neurological diseases, and drug abuse and addiction.
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| Pitt MSNBIO 2102 - Systems Neurobiology |
| This course is a component of the introductory graduate sequence designed to provide an overview of neuroscience. This course provides an introduction to the structure of the mammalian nervous system and to the functional organization of sensory systems, motor systems, regulatory systems, and systems involved in higher brain functions. It is taught primarily in a lecture format with some laboratory work.
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| Pitt NROSCI 2012 - Neurophysiology |
| This course examines the electrical properties of nerve cells and the mechanisms by which nerve cells communicate. The following topics will be covered: electrical principles used by nerve cells, the basis of the resting potential, the function of voltage-dependent ionic channels, the mechanisms by which action potentials are generated, neurotransmitter receptor function, and the physiology of fast synaptic communication.
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| Pitt NROSCI 2107 - Current Research Neural Basis of Cognition |
| Presentations of current research by students and faculty of the Center for the Neural Basis of Cognition, and by visiting researchers from other universities. Areas of cognition covered include perception, memory, language, attention, motor control, and executive functions. Disorders of cognition as well as developmental issues are considered. Methodologies include single neuron recording studies, function al brain imaging studies, computational modeling studies, and behavioral investigations using normal populations and individuals with cognitive disorders.
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| Pitt PHYS 2274 - Computational Physics |
| The main subject matter of this course will be a survey of group theory methods and their applications in various fields of physics. Selected topics involving analytic functions, operator algebra, and solutions of the differential and integral equations of physics will be addressed. Some numerical analysis and computational work will also be incorporated.
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| Pitt PHYS 2541 - Statistical Physics |
| This is the first term of a 2-term course with emphasis on statistical mechanics. Discussion of microcanonical, canonical, and grand canonical ensembles, the passage to quantum mechanics, and the use of density matrix. The Gibbs approach to the second law. Fermi-Dirac and Bose-Einstein statistics, in both weak and strong degeneracy approximations. Transport phenomena including the fluctuation dissipation theorem and the master equation.
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| Pitt STAT 2631 - Theory of Statistics |
| This is an introductory graduate course in the theory of statistical estimation. The following topics will be covered. The use of orthogonal transformations in statistical distribution theory, distribution of quadratic forms, the theory of linear estimation, the general theory of estimation and estimation from a decision theoretic point of view.
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| Pitt STAT 2711 - Probability Theory I |
| This course begins with an introduction to Lebesque integral. Then distribution functions, probability measures and random variables are introduced. Convergence concepts and topics from the laws of large numbers and random series are also covered.
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