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Graduate Calendar
Winter 2019

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All courses listed will not necessarily be offered in any given year.


62-510. Functions of a Real Variable I
Lebesgue measure, abstract measure, integration, monotone and dominated convergence theorems, Radon-Nikodym theorem, Hahn decomposition theorem, Fubini's theorem, Lp spaces.

62-511. Functions of a Real Variable II
Metric spaces, topological spaces, compactness, Stone-Weierstrass and Ascoli theorems, Baire category theorem, classical Banach spaces.

62-512. Functional Analysis I
Normed linear spaces and examples, Hahn-Banach theorem, open mapping theorem, principle of uniform boundedness, weak and weak* topologies on Banach spaces, Hilbert spaces and bounded linear operators on Hilbert spaces.

62-513. Functional Analysis II
Banach algebras and spectral theory, operator theory, C*-algebras and their representations, elementary von Neumann algebra theory.

62-520. Abstract Algebra
Elements of group theory are explored including such topics as: the Sylow Theorems, classification of groups of low order, Jordan-Holder Theorem, solvable groups, nilpotent groups, groups in terms of generators and relation, representations of groups, basic operations on representations, orthogonality relations

62-521. Ring Theory and Modules
This course is designed to introduce students to the structure theory of general rings and their modules. It will provide an appropriate foundation for more advanced graduate material in algebra at the doctoral level and will be an excellent preparation for doctoral comprehensive examinations. Topics covered will include: semisimple rings, Wedderburn-Artin Theorem, modules over a principal ideal domain, projective, injective and flat modules, introduction to homology theory.

62-522. Introduction to Field Theory
This course will cover polynomial rings, splitting fields, the fundamental theorem of Galois theory, Galois' criterion for solvability by radicals, algebraically closed fields and finite fields. (Prerequisites: 62-221 and 62-321) (Cross-listed with 62-422.)

62-523. Lie Algebras
Engel's Theorem, Lie's Theorem, criterion for semi simplicity, root space decomposition, universal enveloping algebra, PBW basis, representation theory, finite dimensional modules, Harish-Chandra's Theorem.

62-525. Matrix Algebra and Analysis
Aspects of measure theory and probability, convergence theorems for integrations and expectations, moments and inequalities, construction of Lebesgue-Stieltjes measure, Riemann-Stieltjes integral, comparison of Riemann and Lebesgue integrals, introduction to complex variable, contour integration, characteristics functions, elementary theorems on linear and matrix algebra, generalized and conditional inverses, distributions of quadratic forms. This course is designed for graduate students in Statistics.

62-530. General Topology
Elementary concepts of topology, product and quotient spaces, continuity and homeomorphisms, nets and filters, separation and countability, compactness, connectedness.

62-551. Advanced Linear Programming
By presenting results and their proofs, the student will acquire a solid understanding of the theory, algorithms and applications of linear programming. This course is a prerequisite for more advanced courses on integer programming, combinatorial optimization and networks flows. Topics emphasized include: formulations of linear programming problems, convex sets and convex functions, separation theorem, Farkas' lemma, duality theory, economic interpretation of duality, optimality conditions, primal and dual simplex algorithms, cycling, sensitivity analysis, interior-point methods and central path, primal-dual methods, convergence results.

62-552. Nonlinear Programming
This course will provide an introduction to the field of nonlinear programming. By presenting results and their proofs, the student will acquire a solid understanding of the theory behind most algorithms for solving nonlinear optimization problems. He/she will also acquire the knowledge and skills needed to conduct research in this area. Topics covered will include: unconstrained optimization, necessary and sufficient conditions for optimality, convex sets and convex functions, steepest descent method, Newton's method, conjugate gradient methods, quasi-Newton's methods, separation theorem and Farkas' lemma, Karush-Kuhn-Tucker conditions, constraint qualification conditions, duality theory, Barrier methods, and quadratic programming.

62-553. Integer Programming
This course will provide the student with a rigorous introduction to the field of integer programming. Topics covered will include: modelling with integer variables, elements of computational complexity theory, elements of polyhedral theory, total unimodularity, branch and bound methods, cutting plane methods, implicit enumeration, Bender decomposition, dynamic programming, lagrangian relaxation, knapsack problems, set covering/packing/partitioning problems, heuristic methods.

62-554 Combinatorial Optimization
This course will provide a rigorous introduction to combinatorial optimization. The student will develop a solid understanding of the theory, algorithms and applications of these problems and their connections to integer programming, linear programming and complexity theory. Topics will include: formulation of combinatorial optimization problems, polytopes and polyhedra, elements of computational complexity theory, shortest paths, bipartite and non-bipartite matchings, max-flow min-cut theorem, multi-commodity flow problems, clique and coloring problems, perfect graphs, traveling salesman problem, spanning trees, matroids.

62-561. Partial Differential Equations
First-order equations, classification of second-order equations, canonical forms and general solutions of second-order equations, diffusion equations, Laplace equations, the maximum principle and uniqueness for the Dirichlet problem, wave equations, Riemann's method for linear hyperbolic equation, Green's functions and transform methods.

62-568. Numerical Analysis I
General error analysis, direct solution of linear algebraic equations, iterative solution of linear equations, algebraic eigenvalue problems, numerical solution of a system of nonlinear equations, error analysis.

62-569. Numerical Analysis II
Interpolation and approximation, numerical integration and differentiation, finite differences. Numerical solution of ordinary and partial differential equations using finite differences.

62-582. Portfolio Optimization
This is a first course on Markowitz mean-variance portfolio optimization. The course will cover quadratic programming, parametric quadratic programming, the efficient frontier, the capital asset pricing model, Sharpe ratios and implied risk-free returns, portfolio optimization with constraints, and quadratic programming solution algorithms; also covered are professional writing and presentation skills and the use of optimization software. (Prerequisite: 62-221.) (Cross-listed with 62-482.)

62-598. Special Topics

62-796. Major Paper

62-797. Thesis (M.Sc.)

62-798. Dissertation (Phd)
Graded as pass/fail.


65-540. Theory of Probability
Basic probability model, random variables and their distributions, expectation, convergence of random variables and their distributions, independence and conditional dependence. Zero-one laws, characteristic functions, generating functions, Law of large numbers, Central Limit Theorem.

65-541. Stochastic Processes
Discrete and continuous time Markov processes, renewal theory, branching processes, Brownian motion.

65-542. Advanced Mathematical Statistics
A review of probability theory, transformations and expectations, common families of distributions, inequalities and identities, properties of a random sample, data reduction and best estimation strategies, asymptotic approximation.

65-543. Statistical Inference
Measure of performance, pure significance test and formal hypothesis testing, interval estimation, asymptotic evaluations, analysis of variance and regression, analysis of categorical data.

65-544. Multivariate Analysis
This course is aimed at giving theoretical and methodological background on inference procedure for the analysis of multivariate continuous data mainly under the assumption of normaility.

65-546. Statistical Data Analysis
This course takes a computer-oriented approach to equip students with the experience of data analysis, beginning with designing of experiment to presentation of results. Depending on the background of the students, different topics will be emphasized.

65-548. Non-parametric Statistics
Nonparametric tests including Wilcoxon, Mann-Whitney, Smirnov, Fisher's exact test, Cox and Stuart test for trend, runs test. Estimation. Theory and applications.

65-549. Discrete Multivariate Analysis
This course is aimed at giving theoretical and methodological background for the analysis of discrete data mainly in the form of contingency tables. Other discrete models as part of the generalized linear models may be covered.

65-550. Generalized Linear Models
This course is aimed at giving theoretical and methodological background for the analysis of discrete or continuous data using the generalized linear models and other semi-parametric models where full distributional assumptions cannot be justified.

65-552. Experimental Design
Factorial designs with and without interactions, randomized block, Latin square, balanced incomplete block, nested design, confounding factorial and other designs. Fixed, random and mixed models.

65-554. Theory of Sampling and Surveys
Sampling methods including simple random, stratified, cluster, PPS and multistage, ratio and regression estimates. Theory and applications.

65-555. Regression Analysis
Simple and multiple linear regression, inference on regression parameters, residual analysis, stepwise regression, polynomial regression, diagnostics and remedial measures for multicollinearity and influential observations, weighted least squares, logistic regression, nonlinear regression.

65-559. Topics in Statistics
Topics offered may include queueing theory, statistical quality control, statistical consulting, survival analysis, time series analysis, decision theory, and Bayesian analysis.

65-796. Major Paper

65-797. Thesis (MSc.)

65-798. Dissertation (Phd)
Graded as pass/fail.


97-501. Probability for Risk and Actuarial Science
Topics include discrete and continuous probability univariate and multivariate distributions, conditional and marginal distributions. Moments. Generating functions. Transformation of random variables. Order Statistics. Central Limit Theorem. Course is restricted to students in Master of Actuarial Science. (Prerequisite: Integral Calculus.)

97-502. Financial Mathematics, Theory of Interest
Topics include interest rates, force of interest, equations of value, annuities, amortization, bonds, cash flows, yield, rate of return, term structure, forward interest rates, spot rates, duration, convexity, immunization,. Course is restricted to students in the Master of Actuarial Science program

97-503. Derivatives Markets I
Topics include financial derivatives, short selling, European and American options, hedging, arbitrage, forwards, futures, swaps, bond price models, binomial model, binomial model. Course is restricted to students in the Master of Actuarial Science program. Prerequisite: 97-501, 97-502.

97-504. Derivatives Markets II
Topics include Black Scholes option pricing, exotic options, Brownian motion, Ito integrals. Stochastic models. Course is restricted to students in the Master of Actuarial Science program. Prerequisite: 97-503.

97-505. Life Contingencies I
Topics include life contingencies, survival distributions and life tables, life annuities, net premiums, premium reserves. Course is restricted to students in the Master of Actuarial Science program. Prerequisite: 97-502.

97-506. Life Contingencies II
This course builds on the foundational life contingencies knowledge developed in 97-505 and extends into advanced topics in the field. Topics include advanced life contingencies, risk theory, survival models, construction of mortality tables. Course is restricted to students in the Master of Actuarial Science program. Prerequisite: 97-505.

97-507. Special Topics in Actuarial Science
This course allows students to select and explore in depth relevant topics of interest. Topics selected may include areas such as: loss models (severity models, frequency models, aggregate models, losses, risk measures, empirical models, parametric models, failure time, loss distributions), Bayesian analysis, simulation, etc. Course is restricted to students in the Master of Actuarial Science program. Prerequisite: 97-501.

97-510. Microeconomics
This course provides an introduction to microeconomics , including the tools necessary to begin to understand and evaluate how resources are allocated in a market economy. Specific topics include how markets function, theories of the business firm, of consumer behaviour and of income distribution, supply and demand, the economic roles of labour unions and government. This course is restricted to students in the Master of Actuarial Science program.

97-511. Macroeconomics
This course provides an introduction to macroeconomics with an emphasis on measuring and explaining economic aggregates such as the GDP and the level of prices and employment. Topics include the role of money and financial institutions, the impact of international trade, the policy options available to governments for coping with inflation and unemployment. This course is restricted to students in the Master of Actuarial Science program. Prerequisite: 97-510.

97-520. Regression and Time Series
This course introduces regression and time series analyses. Topics include multiple linear regression, least squares, model fitting, estimation, testing, matrix formulation, indicator variables, logistic regression, residual analysis, prediction intervals, times series, autoregressive models, moving average models, ARIMA models, fitting models, estimation and forecasting. Course is restricted to students in the Master of Actuarial Science program. Prerequisite: 97-501.