93 graduate courses in Waterloo’s course catalog for Winter 2020, from UW Open Data. uwloo can’t read that term’s Graduate Calendar, so this stands in for it; the calendar itself is in Waterloo’s archive.
CS 600Fundamentals of Computer Science for Data Science
This course is designed for Data Science MMath students who do not have an undergraduate degree in Computer Science. It provides these students with the background needed to take Data Science graduate courses with a strong computational flavour. Topics include the following. The design and operation of processors. Memory hierarchies. Basic concepts of operating systems, concurrency, parallelism, and networks. Elementary data structures and their use in algorithm design and analysis.
Introduces non-CS major students to infrastructure for data-sensitive analytics, with a focus on abstractions, frameworks, and algorithms that allow developers to distribute computations across many machines. Topics include core concepts (partitioning, replication, locality, consistency), computational models (MapReduce, dataflows, stream processing, bulk-synchronous parallel), and applications.
An overview of basic security and privacy principles relevant in the design and use of applications in health settings. Program security, operating system security, network security, data security, and issues related to the management of security and privacy policies are introduced. Master of Health Informatics students only.
CS 636Introduction to Computer Networks and Distributed Computer Systems
An introduction to computer networks, Internet protocols, and distributed systems. Layered models, naming, addressing and routing, reliable communication, security, and elements of distributed system design. Master of Health Informatics students only.
A user-oriented approach to the management of large collections of data. Relational database technology, relational algebra, SQL, database views, transactions, data modelling methodology, entity-relationship models. Introduction to several current topics in database research, such as warehousing, data mining, managing data streams, data cleaning, data integration, and distributed/parallel databases. Master of Health Informatics students only.
An exposure to important concepts and issues in contemporary programming languages. Data types, abstraction, and polymorphism. Program structure. Lambda calculus and functional programming, logic programming, object-oriented programming. Semantics of programming languages. Critical comparison of language features and programming methodologies using examples drawn from a variety of programming languages including Lisp, Prolog, ML, Ada, Smalltalk, Icon, APL, and Lucid. Programming assignments involve the use of some of these languages.
Phases of compilation. Lexical analysis and a review of parsing. Compiler-compilers and translator writing systems. LEX and YACC. Scope rules, block structure, and symbol tables. Runtime stack management. Parameter passage mechanisms. Stack storage organization and templates. Heap storage management. Intermediate code. Code generation. Macros.
CS 645Software Requirements Specification and Analysis
Introduces students to the requirements definition phase of software development: Models, notations, and processes for software requirements identification, representation, analysis, and validation. Cost estimation from early documents and specifications.
Introduces students to the design, implementation, and evolution phases of software development. Software design processes, methods, and notation. Implementation of designs. Evolution of designs and implementations. Management of design activities.
CS 647Software Testing, Quality Assurance and Maintenance
Introduces students to systematic testing of software systems. Software verification, reviews, metrics, quality assurance, and prediction of software reliability and availability. Related management issues.
The objective of this course is to introduce students to fundamentals of building a relational database management system. The course focuses on the database engine core technology by studying topics such as storage management (data layout, disk-based data structures), indexing, query processing algorithms, query optimization, transactional concurrency control, logging and recovery. Preference will be given to CS graduate students. All other require permission from the school.
An introduction to the fundamental theories, methods, and research in the design and evaluation of description: novel computational artifacts designed to meet real-world human needs.
The course is intended to provide the student with an appreciation of modern computer design and its relation to system architecture, compiler technology and operating system functionality. The course places an emphasis on design based on the measurement of performance and its dependency on parallelism, efficiency, latency and resource utilization.
Introduces students to infrastructure for data-intensive computing, with a focus on abstractions, frameworks, and algorithms that allow developers to distribute computations across many machines. Topics include core concepts (partitioning, replication, locality, consistency), computational models (MapReduce, dataflows, stream processing, bulk-synchronous parallel), and applications.
Intended to give students experience with tools and techniques of real-time programming, this course includes not only issues of microcomputer architecture and a real-time programming language and operating system, but also hands-on experience programming a microcomputer for applications such as process control, data acquisition and communication. .Preference will be given to CS graduate students. All others require approval from the department.
An introduction to distributed systems, emphasizing the multiple levels of software in such systems. Specific topics include fundamentals of data communications, network architecture and protocols, local-area networks, concurrency control in distributed systems, recovery in distributed systems, and clock synchronization. Preference will be given to CS graduate students. All others require approval from the department.
An introduction to network architectures and protocols, placing emphasis on protocols used in the Internet. Specific topics include application layer protocols, network programming, transport protocols, routing, multicast, data link layer issues, multimedia networking, network security, and network management.
Basic techniques of system performance evaluation. Specific topics include: performance modeling, discrete event simulation, verification and validation of simulation models, analysis of simulation output, analysis of single server queue and queueing networks, modeling of computer systems, networks, and other queueing or non-queueing systems.
Security and privacy issues in various aspects of computing. Specific topics include: comparing security and privacy, program security, writing secure programs, controls against program threats, operating system security, formal security models, network security, Internet application security and privacy, privacy-enhancing technologies, database security and privacy, inference data mining, security policies, physical security, economics of security, and legal and ethical issues. (Note: Knowledge of operating systems equivalent to that obtained from CS 350 is assumed.)
Languages and their representations. Grammars-Chomsky hierarchy. Regular sets and sequential machines. Context-free grammars-normal forms, basic properties. Pushdown automata and transducers. Operations on languages. Undecidable problems in language theory. Applications to the design of programming languages and compiler construction.
The classification of problems according to the computational resources required for their solution, with emphasis on properties of feasible computations rather than on specific algorithms. Topics include: time and space complexity, tractable and intractable problems, computation using randomness, parallel computation.
Design of good algorithms and analysis of the resources they consume. Lower bounds on the resource requirements of algorithms to compute certain functions. Problems from the following areas are discussed in this light: sorting and order statistics, data structures, arithmetic computations, the NP-complete problems.
Basic concepts and implementation of numerical linear algebra techniques and their use in solving application problems. Special methods for solving linear systems having special features. Direct methods: symmetric, positive definite, band, general sparse structures, ordering methods. Iterative methods: Jacobi, Gauss-Seidel, SOR, conjugate gradient. Computing and using orthogonal factorizations of matrices. QR and SVD methods for solving least squares problems. Eigenvalue and singular value decompositions. Computation and uses of these decompositions in practice.
The interaction of financial modes, numerical methods, and computing environments. Basic computational aspects of option pricing and hedging. Numerical methods for stochastic differential equations, strong and weak convergence. Generating correlated random numbers. Time-stepping methods. Finite difference methods for Black-Scholes equation. Discretization, stability, convergence. Methods for portfolio optimization, effect of data errors on portfolio weights. (Heldwith CS 476).
Introduction to modeling and algorithmic techniques for machines to learn concepts from data. Generalization: underfitting, overfitting, cross-validation. Tasks: classification, regression, clustering. Optimization-based learning: loss minimization, regularization. Statistical learning: maximum likelihood, Bayesian learning. Algorithms: nearest neighbor, (generalized) linear regression, mixtures of Gaussians, Gaussian processes, kernel methods, support vector machines, deep learning, sequence learning, ensemble techniques. Large scale learning: distributed learning and stream learning. Applications: Natural language processing, computer vision, data mining, human computer interaction, information retrieval.
CS 682Computational Techniques in Biological Sequence Analysis
Computer science principles and algortihms in biological sequence analysis. Topics include algotithms for sequence comparison, for large-scale database search in biological databases, for sequence assembly, for evolutionary tree reconstruction, for identifying important featrues in DNA and RNA sequences, and underlying computational techniques for understanding strings and trees and for making probabilistic inferences. (Heldwith CS 482).
Introduction to image and vision understanding by computer. Camera-system geometry, image formation and lighting, and image acquisition. Basic visual processes for recognition of edges, regions, lines, and surfaces. Processing of stereo images, and motion in image sequences. Object recognition. Applications of computer vision systems.
CS 685Machine Learning: Statistical and Computational Foundations
Extracting meaningful patterns from random samples of large data sets. Statistical analysis of the resulting problems. Common algorithm paradigms for such tasks. Central concepts: VC-dimension, Margins of classifier, Sparsity and description length. Performance guarantees: Generalization bounds, data dependent error bounds and computational complexity of learning algorithms. Common paradigms: Neural networks, Kernel methods and Support Vector machines, Applications to Data Mining.
Goals and methods of artificial intelligence. Methods of general problem solving. Introduction to mathematical logic Mechanical theorem proving. Game playing. Natural language processing. Preference will be given to CS graduate students. All others require approval from the department. Department approval will be by Undergraduate Advisor.
An introduction to the use of computers for symbolic mathematical computation, involving traditional mathematical computations such as solving linear equations (exactly), analytic differentiation and integration of functions, and analytic solution of differential equations.
Software and hardware for interactive computer graphics. Implementation of device drivers, 3-D transformations, clipping, perspective, and input routines. Data structures, hidden surface removal, colour shading techniques, and some additional topics will be covered. Preference will be given to CS graduate students. All others require approval from the department.
This is an individual study course carried out under the supervision of a Computer Science faculty member. The topic should be agreed upon by both the student and the instructor. This is a credit/no credit course. Department permission will be by Coordinator of Graduate Studies.
This is an individual study course carried out under the supervision of a Computer Science faculty member. The topic should be agreed upon by both the student and the instructor. This is a grade course. Department permission will be by Coordinator of Graduate Studies.
This course is designed to consider the problems encountered by individuals, organizations and society as computer technology is adopted, with a view towards assessing possible courses of action.
Research and life skills that can help graduate students improve their academic and interpersonal competence-research methodologies, library research skills, creative and critical thinking, time management, stress management, technical reading skills, listening skills, oral communication, writing and publishing in computer science, jobs in academica versus industry.
This number is used for courses being offered on a temporary basis. Such a course may be available only once, for example to take advantage of a visiting professor's expertise, or may be offered experimentally until it is determined whether of not the course should become part of the regular course offerings. It may also be used for an individual study course carried out under the supervision of a Computer Science faculty member with the approval from the Associate Chair, Graduate Studies. This is a grade course. Preference will be given to CS graduate students. All others require approval of the Department.
Project-oriented course that covers the implementation of relational database management systems. Topics include database system architecture; managing primary and secondary storage; query processing; metadata and catalog management; language processing; query optimization and plan generation; concurrency; failures and recovery; extensibility; client-server interactions.
Management of non-relational databases, such as multimedia databases, text databases, temporal databases or spatial databases. Each offering will target a specific type of data. Topics include rationale for and common applications of non-relational database management; systems and standards; the abstract data model; data definition and manipulation languages; data storage and indexing; query processing and optimization; updates and transaction management.
Algorithms and architectures used in parallel database management systems, with a focus on relational systems. Topics include system architectures; parallel and distributed query processing; federated dtabase systems; distributed transactions; data replication.
An overview of relational databases and how they are used; exposure to relational database technology. Fundamentals of transactions. Database views. Introductions to two or three alternative data models and systems, such as those used for structured text, spatial data, multimedia data, and information retrieval. Introduction to several current topics in database research, such as warehousing, data mining, managing data streams, data cleaning, data integration, and distributed databases.
Project-oriented course that covers optimizing compilers and the implementation of advanced programming language features. Topics include intermediate representations; data-flow, dependence, and alias analysis; optimizing transforms, register allocation, instruction scheduling; memory management, garbage collection, threads, concurrency; single and multiple inheritance, generics, templates, type inference.
Application of formal methods to the verification of computer-based systems. Algebraic and automata preliminaries. Temporal logic and model checking. Decision procedures. Mechanized theorem proving. Advanced topics chosen by the instructor.
A project-oriented course that covers the concepts in software architecture. Topics include basic concepts (components and connectors, rationale, views, architectural diagrams, team implictions, evolution, size considerations), theory underlying softeware architecture, extraction of architecture from implementation artifacts, architecture of web-based and enterprise systems, clustering of subsystems, visualization approaches to software architecture, comprehension and cognitive aspects of software architecture.
This course provides a broad discussion of advanced topics in modern distributed systems. The course will discuss the state of the art of these systems and how and why they reached there. The course will discuss current data center hardware and software architecture, communication middleware, and how modern systems can tolerate failures, maintain data consistency, scale, leverage the new hardware capabilities, and exploit highly concurrent hardware.
CS 755System and Network Architectures and Implementation
Distributed, multi-user applications are designed and implemented using many underlying technologies that must be coordinated to provide important features such as robustness, scalability, manageability, ubiquitous access, privacy, security, authentication, and role-based access control, to name only a few. The network supporting the application may be crucial to its successful implementation. The application logic itself is likely implemented in a number of languages and programming environments. Students will be provided with an advanced overview of current networking and distributed systems topics, and will apply it to case studies drawn form consumer internet applications, enterprise systems, and medical and healthcare systems.
Cryptographic protocols and their application to secure communication, especially in a network setting. Identification and entity authentication; protocols for key establishment, transport, agreement and maintenance; secret sharing, broadcast encryption, tracing schemes; certificates, public-key infrastructure, PGP.
Introduction to the design and analysis of algorithms that make use of randomization. Topics include review of basic probabiloity and introduction to randomized algorithms; game theoretic techniques; uses of Markov and Chebyshev inequalities; tail inequalities; Markov chains and random walks; algebraic techniques; data structures and graph algorithms.
Further exposure to the design, analysis and application of algorithms for problems defined on graphs. Topics include planarity testing and embedding; classes of planar graphs and fast algorithms for them; trees and tree-like graphs (bounded pathwidth and treewidth); perfect graphs and intersection graphs.
Introduction to the design, analysis and application of algorithms for geometric problems. Topics include convex hull algorithms in two and three dimensions; Voronoi diagrams, Delaunay triangulations, and their applications; linear programming in low dimensions; line segments, planar subdivision, and polygons; range searching.
Further exposure to the classification of problems based on their computational requirements and to mathematical tools designed to explore the structural consequences of such classifications. Topics include relativization, alternation, provably intractable problems, feasible parallel computation; fixed-parameter tractability and the W-hierarchy; Kolmogorov complexity, including algorithmic and algorithmic prefix complexity and their applications.
Fundamental problems of elementary and algebraic number theory from an algorithmic and computational complexity point of view with emphasis placed on analysis of algorithms. Topics include basic arithmetic algorithms; computation over finite fields; primality testing; algorithms for integer factorization; algorithms in number fields.
Fundamentals of quantum information theory including states, measurements, operations, and their representations as matrices; measures of distance between quantum states and operations; quantum Shannon theory including von Neumann entropy, quantum noiseless coding, strong subadditivity of von Neuman entropy, Holevo's Theorem, and capabilities of quantum channels; theory of entanglement including measures of entanglement, entanglement transformation, and classifications of mixed-state entanglement; other topics in quantum information as time permits.
The course provides extended background in mathematical logic and its applications to various branches of computer science. It covers some fundamental concepts such as soundness and completeness theorems, compactness, the expressive power of a logic and the computational complexity of its basic decision problems. These concepts are being demonstrated on first order logic and modal logics. Finally the course discusses examples of applications of these concepts and tools to formal reasoning about programs and about hardware, to data bases, and to knowledge representation.
Review of basics of quantum information and computational complexity; Simple quantum algorithms; Quantum Fourier transform and Shor factoring algorithm: Amplitude amplification, Grover search algorithm and its optimality; Completely positive trace-preserving maps and Kraus representation; Non-locality and communication complexity; Physical realizations of quantum computation: requirements and examples; Quantum error-correction, including CSS codes, and elements of fault-tolerant computation; Quantum cryptography; Security proofs of quantum key distribution protocols; Quantum proof systems. Familiarity with theoretical computer science or quantum mechanics will also be an asset, though most students will not be familiar with both.
Introduction to basic algorithms and techniques for numerical computing. Error analysis, interpolation (including splines), numerical differentiation and integration, numerical linear algebra (including methods for linear systems, eigenvalue problems, and the singular value decomposition), root finding for nonlinear equations and systems, numerical ordinary differential equations, and approximation methods (including least squares, orthogonal polynomials, and Fourier transforms).
Option pricing, hedging, model calibration, and portfolio optimization will be discussed. Computational methods, including PDE methods, Monte Carlo, and mathematical programming will be presented.
Techniques for obtaining maximum parallelism in various numberical algorithms, especially those occurring when solving matrix problems and partial differential equations, and the subsequent mapping onto the computer. Topics include: parallel architecture and performance models; message passing/shared memory programming; matrix computations; fast Fourier transform; graph partitioning; domain decomposition methods.
CS 778Numerical Solution of Partial Differential Equations
Discretization methods for partial differential equations, including finite difference, finite volume and finite element methods. Application to elliptic, hyperbolic and parabolic equations. Convergence and stability issues, properties of discrete equations, and treatment of non-linearities. Stiffness matrix assembly and use of sparse matric software. Students should have completed a course in numerical computation at the undergraduate level.
Spline theory and recent developments in techniques for representing, manipulating and rendering curves and surfaces constructed from splines in a graphic environment. Applications of interest include computer-aided design, synthetic image generation and animation.
A deep investigation into fundamental problems of symbolic computation. These may include algorithms for linear, non-linear, and differential systems of symbolic equations, symbolic integration, factoring polynomials, and symbolic-numeric algorithms. Covers the basic data types and structures for algebraic objects and operations. Issues in the design of computer algebra systems.
In computer graphics, 3D models are rendered through an ideal camera to a 2D image. The virtual camera is a powerful tool, controlling not only perspective and the optical path, but colour mapping as well. This course examines the notion of "colour" at different stages of the computer graphics pipeline, and describes techniques for modelling and managing colour through the virtual camera analogy. Topics include illumination, the virtual camera, colour spaces, gamut mapping and colour management. Implementation of the colour reproduction and gamut-mapping algorithms demands a number of numerical and statistical methods, including multidimensional interpolation and approximation, computational geometry, splines, principal components anlysis and optimization.
This course examines, from an algorithmic point of view, the pattern discovery techniques that are currently used to extract the functional knowledge hidden in biomolecular data that is derived from DNA, RNA, proteins and their reaction products. Topics include: DNA sequence analysis, RNA structure prediction techniques, protein motif discovery techniques, protein structure prediction, analysis of expression data.
This course addresses the computer (in silico) simulation of biophysical processes involved in the interaction of light with organic and inorganic matter. Computer models used to simulate these processes are presented, and key stages of the simulation pipeline, such as data gathering, design constraints and evaluation methodologies, are examined in detail. This course also includes discussions of open problems and current trends in the computer simulation of biophysical processes. Application fields include, but are not limited to computer graphics, remote sensing, biology and biomedical optics.
Computer understanding and generation of natural (i.e., human) languages. Basic topics in natural language understanding (syntax, semantics, pragmatics, connected discourse). Selected applications (e.g., automated language generation, machine translation, natural language processing and the Web).
Intelligence in interfaces-natural language processing, plan recognition, dialogue, generation, user modeling. Interfaces to intelligent systems-intelligent agents and multi-agent systems, information processing and data mining, knowledge-based systems.
CS 786Probabilistic Inference and Machine Learning
Covers the fundamental principles of probabilistic inference and computational learning systems. Topics include Bayes decision and utility theory, Monte Carlo and Markov chain Monte Carlo methods; learning with complete data; Bayesian networks, Markov random fields and factor graphs; models; learning with incomplete data; computational learning and PAC learning theory.
Fundamental problems in computational vision where efficient and robust algorithms can be applied. Topics include image formation; linear systems and Fourier theory; image registration; feature detection; fitting models to data; optical flow; structure from motion; steriopsis; object recognition; high-level vision.
Project-oriented course that covers high-performance image synthesis using techniques for both real-time interactive systems and offline physically-based rendering. Topics include numerical techniques; visual perception and light; mathematical models of rendering; global illumination algorithms; real-time rendering.
Graphics input devices and interaction techniques. The pragmatic factors of various physical, logical and virtual devices, human factors of interactive systems, interaction dialogue managers.
Fundamentals of non-photorealistic computer depiction. Problems of style, abstraction and spatial and temporal coherence. Stroke-based rendering and simulation of traditional artistic media. Stylized processing of images and video. Real-time non-photorealistic rendering of 3D models; toon shaders and contour rendering. Geometric art and ornamental design.
CS 792Data Structures and Standards in Health Informatics
This course focuses on health data as a key component of all health informatics systems. Topics include ontologies and other classification taxonomies found in health systems, data standards (with a focus on Canadian implementations of international standards), privacy and security of health data, client/patient assessment tools, and ethical considerations.
Techniques for formulating data science models as optimization problems. Algorithms for solving data science problems with an emphasis on scalability, efficiency and parallelizability including gradient-descent based algorithms, derivative-free algorithms, and randomized algorithms. Theoretical analyses of algorithms and approaches for recognizing the most suitable algorithm for solving a particular problem.