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class-data.csv
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courseCode;; courseName;; courseDescription;; coursePrereqs;; courseLevel;; courseSeasons
// ";;" is used for delimiter for headings: take note
// ", " is used for delimiter for coursePrereqs and courseSeasons
// make sure there are no trailing lines at the end of the file!!
//
// HS
HS;; High School;; Any course connected to this node has no prerequisites;; ;; 1;;
// Transfer Credits
CHEM 120;; Physical and Chemical Properties of Matter;; The stoichiometry of compounds and chemical reactions. Properties of gases. Periodicity and chemical bonding. Energy changes in chemical systems. Electronic structure of atoms and molecules; correlation with the chemical reactivity of common elements, inorganic and organic compounds.;; HS;; 2;; F, W
CHEM 123;; Chemical Reactions, Equilibria and Kinetics;; Properties of liquids and solutions. Introduction to chemical equilibria. Principles of acid-base equilibria, solubility and electrochemical processes. Chemical kinetics.;; CHEM 120;; 3;; W, S
// 1A
MATH 145;; Algebra (Advanced Level);; An introduction to the language of mathematics and proof techniques through a study of the basic algebraic systems of mathematics: the integers, the integers modulo n, the rational numbers, the real numbers, the complex numbers and polynomials.;; HS;; 2;; F
MATH 147;; Calculus 1 (Advanced Level);; Absolute values and inequalities. Sequences and their limits. Introduction to series. Limits of functions and continuity. The Intermediate Value theorem and approximate solutions to equations. Derivatives, linear approximation, and Newton's method. The Mean Value theorem and error bounds. Applications of the Mean Value theorem, Taylor polynomials, and Taylor's theorem, Big-O notation. Suitable topics are illustrated using computer software.;; HS;; 2;; F
CS 145;; Designing Functional Programs (Advanced Level);; An introduction to the fundamentals of computer science through the application of elementary programming patterns in the functional style of programming. Syntax and semantics of a functional programming language. Tracing via substitution. Design, testing, and documentation. Linear and nonlinear data structures. Recursive data definitions. Abstraction and encapsulation. Generative and structural recursion. Historical context.;; HS;; 2;; F
PHIL 145;; Critical Thinking;; An analysis of basic types of reasoning, structure of arguments, critical assessment of information, common fallacies, problems of clarity and meaning.;; HS;; 2;; F, W, S
GEOG 101;; Human Geographies;; An introduction to human geography through key subfields of the discipline, examining population change, the rise of cities, our relationship to nature, social inequalities, economic forms, cultural difference and environmental change at the local and global scale.;; HS;; 2;; F, W, S
// 1B
MATH 146;; Linear Algebra 1 (Advanced Level);; Systems of linear equations, matrix algebra, elementary matrices, computational issues. Real n-space, vector spaces and subspaces, basis and dimension, rank of a matrix, linear transformations, and matrix representations. Determinants, eigenvalues and diagonalization, applications.;; MATH 145;; 3;; W
MATH 148;; Calculus 2 (Advanced Level);; Systems of linear equations, matrix algebra, elementary matrices, computational issues. Real n-space, vector spaces and subspaces, basis and dimension, rank of a matrix, linear transformations, and matrix representations. Determinants, eigenvalues and diagonalization, applications.;; MATH 147;; 3;; W
CS 146;; Elementary Algorithm Design and Data Abstraction (Advanced Level);; This course builds on the techniques and patterns learned in CS 135 while making the transition to use an imperative language. It introduces the design and analysis of algorithms, the management of information, and the programming mechanisms and methodologies required in implementations. Topics discussed include iterative and recursive sorting algorithms; lists, stacks, queues, trees, and their application; abstract data types and their implementations.;; CS 145;; 3;; W
PSYCH 101;; Introductory Psychology;; A general survey course designed to provide the student with an understanding of the basic concepts and techniques of modern psychology as a behavioural science.;; HS;; 3;; F, W, S
SPCOM 223;; Public Speaking;; Theory and practice of public speaking. A workshop course involving design and delivery of various kinds of speeches, and the development of organizational, vocal, listening, and critical skills. Students will be videotaped.;; HS;; 3;; F, W, S
// To be done (2A plan)
MATH 245;; Linear Algebra 2 (Advanced Level);; Orthogonal and unitary matrices and transformations. Orthogonal projections, Gram-Schmidt procedure, best approximations, least-squares. Inner products, angles and orthogonality, orthogonal diagonalization, singular value decomposition, applications.;; MATH 146;; 4;; F, S
MATH 247;; Calculus 3 (Advanced Level);; Calculus of functions of several variables. Limits, continuity, differentiability, the chain rule. The gradient vector and the directional derivative. Taylor's formula. Optimization problems. Mappings and the Jacobian. Multiple integrals in various co-ordinate systems.;; MATH 148;; 4;; F, W, S
MATH 249;; Introduction to Combinatorics (Advanced Level);; Introduction to graph theory: colourings, matchings, connectivity, planarity. Introduction to combinatorial analysis: generating series, recurrence relations, binary strings, plane trees.;; MATH 146;; 4;; F, W
STAT 240;; Probability (Advanced Level);; This course provides an introduction to probability models including sample spaces, mutually exclusive and independent events, conditional probability and Bayes' Theorem. The named distributions (Discrete Uniform, Hypergeometric, Binomial, Negative Binomial, Geometric, Poisson, Continuous Uniform, Exponential, Normal (Gaussian), and Multinomial) are used to model real phenomena. Discrete and continuous univariate random variables and their distributions are discussed. Joint probability functions, marginal probability functions, and conditional probability functions of two or more discrete random variables and functions of random variables are also discussed. Students learn how to calculate and interpret means, variances and covariances particularly for the named distributions. The Central Limit Theorem is used to approximate probabilities.;; MATH 145, MATH 147;; 4;; F
CS 245;; Logic and Computation;; Logic as a tool for representation, reasoning, and computation. Propositional and predicate logic. Formalizing the notions of correct and incorrect reasoning, defining what is computable, and exploring the limits of computation. Godel's Incompleteness Theorem. Applications of logic to computer science.;; MATH 146;; 4;; F, W, S
CS 246;; Object-Oriented Software Development;; Introduction to object-oriented programming and to tools and techniques for software development. Designing, coding, debugging, testing, and documenting medium-sized programs: reading specifications and designing software to implement them; selecting appropriate data structures and control structures; writing reusable code; reusing existing code; basic performance issues; debuggers; test suites. (CS 246e is offered in Fall);; CS 146;; 4;; F, W, S
// To be done (2B plan)
STAT 241;; Statistics (Advanced Level);; This course provides a systematic approach to empirical problem solving which will enable students to critically assess the sampling protocol and conclusions of an empirical study including the possible sources of error in the study and whether evidence of a causal relationship can be reasonably concluded. The connection between the attributes of a population and the parameters in the named distributions covered in STAT 230 will be emphasized. Numerical and graphical techniques for summarizing data and checking the fit of a statistical model will be discussed. The method of maximum likelihood will be used to obtain point and interval estimates for the parameters of interest as well as testing hypotheses. The interpretation of confidence intervals and p-values will be emphasized. The Chi-squared and t distributions will be introduced and used to construct confidence intervals and tests of hypotheses including likelihood ratio tests. Contingency tables and Gaussian response models including the two sample Gaussian and simple linear regression will be used as examples.;; MATH 148, STAT 240;; 5;; W
CS 240;; Data Structures and Data Management;; Introduction to widely used and effective methods of data organization, focusing on data structures, their algorithms, and the performance of these algorithms. Specific topics include priority queues, sorting, dictionaries, data structures for text processing.;; CS 245, CS 246, STAT 240;; 5;; F, W, S
CS 241;; Foundations of Sequential Programs;; The relationship between high-level languages and the computer architecture that underlies their implementation, including basic machine architecture, assemblers, specification and translation of programming languages, linkers and loaders, block-structured languages, parameter passing mechanisms, and comparison of programming languages. (CS 241e is offered in Fall);; CS 146;; 4;; F, W, S
CS 251;; Computer Organisation and Design;; Overview of computer organization and performance. Basics of digital logic design. Combinational and sequential elements. Data representation and manipulation. Basics of processor design. Pipelining. Memory hierarchies. Multiprocessors.;; CS 146;; 4;; F, W, S
// Other uncategorised courses (STAT)
STAT 330;; Mathematical Statistics;; This course provides a mathematically rigorous treatment for topics covered in STAT 230 and 231, and to make essential extensions to the multivariate case. Maximum likelihood estimation. Random variables and distribution theory. Generating functions. Functions of random variables. Limiting distributions. Large sample theory of likelihood methods. Likelihood ratio tests. Joint probability (density) functions, marginal probability (density) functions, and conditional probability (density) functions of two or more random variables are discussed. Topics covered include independence of random variables, conditional expectation and the determination of the distribution of functions of random variables using the cumulative distribution method, change of variable and moment generating functions. Properties of the Multinomial and Bivariate Normal distributions are proved. Limiting distributions, including convergence in probability and convergence in distribution, are discussed. Important results, including the Weak Law of Large Numbers, Central Limit Theorem, Slutsky's theorem, and the Delta Method, are introduced with applications. The maximum likelihood method is discussed for the multi-parameter case. Asymptotic properties of the maximum likelihood estimator are examined and used to construct confidence intervals or regions. Tests for simple and composite hypotheses are constructed using the Likelihood Ratio Test.;; MATH 247, STAT 240, STAT 241;; 6;; F, W, S
STAT 331;; Applied Linear Models;; Modeling the relationship between a response variable and several explanatory variables (an output-input system) via regression models. Least squares algorithm for estimation of parameters. Hypothesis testing and prediction. Model diagnostics and improvement. Algorithms for variable selection. Nonlinear regression and other methods.;; MATH 245, STAT 241;; 6;; F, W, S
STAT 332;; Sampling and Experimental Design;; Designing sample surveys. Probability sampling designs. Estimation with elementary designs. Observational and experimental studies. Blocking, randomization, factorial designs. Analysis of variance. Designing for comparison of groups.;; STAT 241;; 6;; F, W, S
STAT 333;; Stochastic Processes 1;; This course provides an introduction to stochastic processes, with an emphasis on regenerative phenomena. Topics cover generating functions, conditional probability distributions and conditional expectation, discrete-time Markov chains with a countable state space, limit distributions for ergodic and absorbing chains, applications including the random walk, the gambler's ruin problem, and the Galton-Watson branching process, an introduction to counting processes, connections between the exponential distribution and Poisson process, and non-homogeneous and compound Poisson processes.;; STAT 240;; 5;; F, W, S
STAT 340;; Stochastic Simulation Methods;; Random variate generation in the univariate and multivariate case, Monte Carlo integration, advanced computer implementation, variance reduction, statistical analysis of simulated data, extensions to challenging simulation problems. Mathematical treatment of the underlying stochastic concepts and proofs.;; CS 145, STAT 240, STAT 241;; 6;; W, S
STAT 341;; Computational Statistics and Data Analysis;; A computationally focused approach to statistical reasoning in the context of real data. Functional programming in R and algorithms will be used to define interesting attributes of finite populations and their sampling characteristics. Computational approaches to inductive inference and the assessment of predictive accuracy.;; MATH 247, STAT 241;; 6;; F, W
STAT 431;; Generalised Linear Models and their Applications;; Review of the normal linear model and maximum likelihood estimation; regression models for binomial, Poisson and multinomial data; generalized linear models; and other topics in regression modelling.;; STAT 330, STAT 331;; 8;; F, W, S
STAT 440;; Computational Inference;; Introduction to and application of computational methods in statistical inference. Monte Carlo evaluation of statistical procedures, exploration of the likelihood function through graphical and optimization techniques. Topics include expectation-maximization, Bootstrapping, Markov Chain Monte Carlo, and other computationally intensive methods.;; STAT 330, STAT 341;; 8;; W, S
STAT 441;; Statistical Learning;; Classification is the problem of predicting a discrete outcome from a set of explanatory variables. Main topics include logistic regression, neural networks, tree-based methods, support vector machines and nearest neighbour methods. Other topics include model assessment, training, and tuning.;; STAT 341, STAT 331;; 8;; F, W
STAT 442;; Data Visualisation;; Visualization methods applied to data. Both human perception and statistical properties inform the methods used to display and visually explore categorical, continuous, time-ordered, map, and high dimensional data. Order and layout effects on tables and graphics. Statistical concepts visually presented include variability, densities, quantiles, conditioning, and hypothesis testing. Interactive graphics include linking, brushing, motion, and the navigation of high dimensional spaces guided via projection indices. Glyphs (e.g., cartoon, statistical, or heatmap) and radial and parallel coordinates.;; STAT 341;; 8;; F, W
STAT 443;; Forecasting;; Modelling techniques for forecasting time series data: smoothing methods, regression including penalty/regularization methods, the Box-Jenkins framework, stationary and non-stationary processes, both with and without seasonal effects. Other topics may include: ARCH/GARCH models, Bayesian methods, dynamic linear models, Markov Chain Monte Carlo simulation, spectral density analysis, and periodograms.;; STAT 331;; 8;; F, W, S
STAT 444;; Statistical Learning - Advanced Regression;; This course introduces modern applied regression methods for continuous response modelling, emphasizing both explainability and predictive power. Topics cover a wide selection of advanced methods useful to address the challenges arising from real-world and high-dimensional data; methods include robust regression, nonparametric regression such as smoothing splines, kernels, additive models, tree-based methods, boosting and bagging, and penalized linear regression methods such as the ridge regression, lasso, and their variants. Students will gain an appreciation of the mathematical and statistical concepts underlying the methods and also computational experience in applying the methods to real data.;; STAT 331, STAT 341;; 8;; W, S
STAT 450;; Estimation and Hypothesis Testing;; Discussion of inference problems under the headings of hypothesis testing and point and interval estimation. Frequentist and Bayesian approaches to inference. Construction and evaluation of tests and estimators. Large sample theory of point estimation.;; STAT 340;; 8;; W
STAT 454;; Sampling Theory and Practice;; Sources of survey error. Probability sampling designs, estimation and efficiency comparisons. Distribution theory and confidence intervals. Generalized regression estimation. Software for survey analysis.;; STAT 332;; 8;; W
// Other uncategorised courses (CS)
CS 341;; Algorithms;; The study of efficient algorithms and effective algorithm design techniques. Program design with emphasis on pragmatic and mathematical aspects of program efficiency. Topics include divide and conquer algorithms, recurrences, greedy algorithms, dynamic programming, graph search and backtrack, problems without algorithms, NP-completeness and its implications.;; CS 240, MATH 249;; 6;; F, W, S
CS 348;; Introduction to Database Management;; The main objective of this course is to introduce students to fundamentals of database technology by studying databases from three viewpoints: those of the database user, the database designer, and the database administrator. It teaches the use of a database management system (DBMS) by treating it as a black box, focusing only on its functionality and its interfaces. Topics include introduction to database systems, relational database systems, database design methodology, SQL and interfaces, database application development, concept of transactions, ODBC, JDBC, database tuning, database administration, and current topics (distributed databases, data warehouses, data mining).;; CS 240;; 6;; F, W, S
CS 350;; Operating Systens;; An introduction to the fundamentals of operating system function, design, and implementation. Topics include concurrency, synchronization, processes, threads, scheduling, memory management, file systems, device management, and security.;; CS 240, CS 241, CS 246, CS 251;; 6;; F, W, S
CS 370;; Numerical Computation;; Principles and practices of basic numerical computation as a key aspect of scientific computation. Visualization of results. Approximation by splines, fast Fourier transforms, solution of linear and nonlinear equations, differential equations, floating point number systems, error, stability. Presented in the context of specific applications to image processing, analysis of data, scientific modeling.;; MATH 148, MATH 146, CS 246;; 6;; F, W, S
CS 371;; Introduction to Computational Mathematics;; A rigorous introduction to the field of computational mathematics. The focus is on the interplay between continuous models and their solution via discrete processes. Topics include pitfalls in computation, solution of linear systems, interpolation, discrete Fourier transforms, and numerical integration. Applications are used as motivation.;; CS 146, MATH 245, MATH 247;; 6;; W, S
CS 442;; Principles of Programming Languages;; 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.;; CS 240;; 8;; W
CS 445;; Software 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.;; CS 350;; 8;; F, W
CS 446;; Software Design and Architectures;; 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 350;; 8;; W, S
CS 448;; Database Systems Implementation;; 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.;; CS 348, CS 350;; 8;; W
CS 451;; Data-Intensive Distributed Computing;; 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.;; CS 341, CS 348, CS 350;; 8;; F, W
CS 454;; Distributed Systems;; 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.;; CS 350;; 8;; W, S
CS 456;; Computer Networks;; 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.;; CS 350;; 8;; F, W, S
CS 458;; Computer Security and Privacy;; 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.;; CS 350;; 8;; F, W, S
CS 466;; Algorithm Design and Analysis;; Algorithmic approaches and methods of assessment that reflect a broad spectrum of criteria, including randomized algorithms, amortized analysis, lower bounds, approximation algorithms, and on-line algorithms. Particular examples will be chosen from different areas of active research and application.;; CS 341;; 8;; F, S
CS 467;; Introduction to Quantum Information Processing;; Basics of computational complexity; basics of quantum information; quantum phenomena; quantum circuits and universality; relationship between quantum and classical complexity classes; simple quantum algorithms; quantum Fourier transform; Shor factoring algorithm; Grover search algorithm; physical realization of quantum computation; error-correction and fault-tolerance; quantum key distribution. (Level >= 4A);; MATH 245;; 8;; W
CS 475;; Computational Linear Algebra;; 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.;; CS 370;; 8;; S
CS 479;; Neural Networks;; An introduction to neural network methods, with some discussion of their relevance to neuroscience. Simple neuron models and networks of neurons. Training feedforward networks for classification or regression. Learning using the backpropagation of errors. Unsupervised learning methods. Optimal linear decoding. Recurrent neural networks. Convolutional neural networks. Advanced topics, including adversarial inputs and biologically plausible learning methods.;; STAT 240, CS 371;; 8;; F, W, S
CS 480;; Introduction to Machine Learning;; 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 neighbour, (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 341, STAT 241;; 8;; F, W, S
CS 485;; Statistical and Computational Foundations of Machine Learning;; Extracting meaningful patterns from random samples of large data sets. Statistical analysis of the resulting problems. Common algorithmic paradigms for such tasks. Central concepts: VC-dimension, margins of a classifier, sparsity and description length, other types of regularization. Performance guarantees: generalization bounds, data dependent error bounds and computational complexity of learning algorithms. Common paradigms: neural networks, kernel methods and support-vector machines, boosting, nearest neighbor classifiers. Applications to data mining.;; CS 341;; 8;; F, W, S
CS 486;; Introduction to Artificial Intelligence;; Goals and methods of artificial intelligence. Methods of general problem solving. Knowledge representation and reasoning. Planning. Reasoning about uncertainty. Machine learning. Multi-agent systems. Natural language processing.;; CS 341, STAT 241;; 8;; F, W, S
// Other uncategorised courses (CO)
CO 255;; Introduction to Optimisation (Advanced Level);; Linear optimization: feasibility theorems, duality, the simplex algorithm. Discrete optimization: integer linear programming, cutting planes, network flows. Continuous optimization: local and global optima, feasible directions, convexity, necessary optimality conditions.;; MATH 245, MATH 247;; 5;; W
CO 330;; Combinatorial Enumeration;; The algebra of formal power series. The combinatorics of the ordinary and exponential generating series. Lagrange's implicit function theorem, applications to the enumeration of permutations, functions, trees and graphs. Integer partitions, geometric methods, enumerating linear transformations. Introduction to the pattern algebra, applications to the enumeration of strings. Lattice paths, Wiener-Hopf factorization. Enumeration under symmetries.;; MATH 249;; 6;; F
CO 342;; Introduction to Graph Theory;; An introduction to some of the key topics in graph theory: connectivity, planarity, and matchings. Connectivity: Menger's theorem, 3-connected graphs. Planarity: Kuratowski's theorem, uniqueness of planar embeddings. Matchings: Review of Konig's theorem, Tutte's theorem.;; MATH 249;; 6;; F, S
CO 351;; Network Flow Theory;; Review of linear programming. Shortest path problems. The max-flow min-cut theorem and applications. Minimum cost flow problems. Network simplex and primal-dual algorithms. Applications to problems of transportation, distribution, job assignments, and critical-path planning.;; CO 255;; 6;; F, S
CO 353;; Computational Discrete Optimisiation;; Formulations of combinatorial optimization problems, greedy algorithms, dynamic programming, branch-and-bound, cutting plane algorithms, decomposition techniques in integer programming, approximation algorithms.;; CO 255;; 6;; F, W, S
CO 367;; Non-Linear Optimisiation;; A course on the fundamentals of nonlinear optimization, including both the mathematical and the computational aspects. Necessary and sufficient optimality conditions for unconstrained and constrained problems. Convexity and its applications. Computational techniques and their analysis.;; MATH 148, CO 255;; 6;; F
CO 430;; Algebraic Enumeration;; The algebra of Laurent series and Lagrange's Implicit function theorem, enumerative theory of planar embeddings (maps). The ring of symmetric functions: Schur functions, orthogonal bases, inner product, Young tableaux, and plane partitions. Non-intersecting paths, sieve methods, partially ordered sets, and Mobius inversion, strings with forbidden substrings, the Cartier-Foata commutation monoid. Introduction to the group algebra of the symmetric group, enumerative applications of sl(2). (CAV >= 80%);; CO 330;; 8;; W
CO 442;; Graph Theory;; Connectivity (Menger's theorem, ear decomposition, and Tutte's wheels theorem) and matchings (Hall's theorem and Tutte's theorem). Flows: integer and group-valued flows, the flow polynomial, the 6-flow theorem. Ramsey theory: upper and lower bounds, explicit constructions. External graph theory: Turan's theorem, the Erdos-Gallai theorem. Probabilistic methods.;; CO 342, MATH 245;; 8;; F
CO 454;; Scheduling;; An overview of practical optimization problems that can be posed as scheduling problems. Characterizations of optimal schedules. Simple and efficient combinatorial algorithms for easy problems. A brief overview of computational complexity, definition of P, NP, NP-complete and NP-hard. Integer programming formulations, the traveling salesman problem, heuristics, dynamic programming, and branch-and-bound approaches. Polynomial-time approximation algorithms.;; MATH 249, CO 255;; 8;; S
CO 456;; Introduction to Game Theory;; A broad introduction to game theory and its applications to the modeling of competition and cooperation in business, economics, and society. Two-person games in strategic form and Nash equilibria. Extensive form games, including multi-stage games. Coalition games and the core. Bayesian games, mechanism design, and auctions.;; MATH 249, CO 255;; 6;; F, W, S
CO 487;; Applied Cryptography;; A broad introduction to cryptography, highlighting the major developments of the past twenty years. Symmetric ciphers, hash functions and data integrity, public-key encryption and digital signatures, key establishment, key management. Applications to Internet security, computer security, communications security, and electronic commerce.;; MATH 145, STAT 240;; 8;; W
// Other uncategorised courses (PMATH)
PMATH 347;; Groups and Rings;; Groups, subgroups, homomorphisms and quotient groups, isomorphism theorems, group actions, Cayley and Lagrange theorems, permutation groups and the fundamental theorem of finite abelian groups. Elementary properties of rings, subrings, ideals, homomorphisms and quotients, isomorphism theorems, polynomial rings, and unique factorization domains.;; MATH 245;; 6;; Q
PMATH 348;; Fields and Galois Theory;; Fields, algebraic and transcendental extensions, minimal polynomials, Eisenstein's criterion, splitting fields, and the structure of finite fields. Sylow theorems and solvable groups. Galois theory. The insolvability of the quintic.;; PMATH 347;; 7;; Q
PMATH 351;; Real Analysis;; Normed and metric spaces, open sets, continuous mappings, sequence and function spaces, completeness, contraction mappings, compactness of metric spaces, finite-dimensional normed spaces, Arzela-Ascoli theorem, existence of solutions of differential equations, Stone-Weierstrass theorem.;; MATH 247;; 6;; Q
PMATH 352;; Complex Analysis;; Analytic functions, Cauchy-Riemann equations, Goursat's theorem, Cauchy's theorems, Morera's theorem, Liouville's theorem, maximum modulus principle, harmonic functions, Schwarz's lemma, isolated singularities, Laurent series, residue theorem.;; MATH 247;; 6;; Q
PMATH 440;; Analytic Number Theory;; Summation methods, analytic theory of the Riemann zeta function, Prime Number Theorem, primitive roots, quadratic reciprocity. Dirichlet characters and infinitude of primes in arithmetic progressions, and assorted topics.;; PMATH 352;; 8;; Q
// end of file