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This course is an introduction to the modelling, analysis, and computer simulation of complex systems. Topics include analytic modelling, discrete event simulation, experimental design, random number generation, and statistical analysis. Prerequisite: CMPT 225, (MACM 101 or (ENSC 251 and ENSC 252)) and STAT 270.

Provides a unified discussion of the fundamental approaches to the problems in artificial intelligence. The topics considered are: representational typology and search methods; game playing, heuristic programming; pattern recognition and classification; theorem-proving; question-answering systems; natural language understanding; computer vision. Prerequisite: CMPT 225 and (MACM 101 or ENSC 251 and ENSC 252)). Students with credit for CMPT 410 may not take this course for further credit.

An advanced course on database systems which covers crash recovery, concurrency control, transaction processing, distributed database systems as the core material and a set of selected topics based on the new developments and research interests, such as object-oriented data models and systems, extended relational systems, deductive database systems, and security and integrity. Prerequisite: CMPT 300 and 354.

Introduction to the essentials of information retrieval and the applications of information retrieval in web search and web information systems. Topics include the major models of information retrieval, similarity search, text content search, link structures and web graphics, web mining and applications, crawling, search engines, and some advanced topics such as spam detection, online advertisement, and fraud detection in online auctions. Prerequisite: CMPT 354.

This course covers the fundamentals of higher level network functionality such as remote procedure/object calls, name/address resolution, network file systems, network security and high speed connectivity/bridging/switching. Prerequisite: CMPT 300 and 371.

The objective of this course is to expose students to basic techniques in algorithm design and analysis. Topics will include greedy algorithms, dynamic programming, advanced data structures, network flows, randomized algorithms. Students with credit for CMPT 706 may not take this course for further credit.

Knowledge representation is the area of Artificial Intelligence concerned with how knowledge can be represented symbolically and manipulated by reasoning programs. This course addresses problems dealing with the design of languages for representing knowledge, the formal interpretation of these languages and the design of computational mechanisms for making inferences. Since much of Artificial Intelligence requires the specification of a large body of domain-specific knowledge, this area lies at the core of AI. Prerequisite: CMPT 310/710 recommended. Cross-listed course with CMPT 411.

Introduction to advanced database system concepts, including query processing, transaction processing, distributed and heterogeneous databases, object-oriented and object-relational databases, data mining and data warehousing, spatial and multimedia systems and Internet information systems.

The student will learn basic concepts and techniques of data mining. Unlike data management required in traditional database applications, data analysis aims to extract useful patterns, trends and knowledge from raw data for decision support. Such information are implicit in the data and must be mined to be useful.

This course examines fundamental principles of software engineering and state-of-the-art techniques for improving the quality of software designs. With an emphasis on methodological aspects and mathematical foundations, the specification, design and test of concurrent and reactive systems is addressed in depth. Students learn how to use formal techniques as a practical tool for the analysis and validation of key system properties in early design stages. Applications focus on high level design of distributed and embedded systems.

Advanced topics in geometric modelling and processing for computer graphics, such as Bezier and B-spline techniques, subdivision curves and surfaces, solid modelling, implicit representation, surface reconstruction, multi-resolution modelling, digital geometry processing (e.g., mesh smoothing, compression, and parameterization), point-based representation, and procedural modelling. Prerequisite: CMPT 361, MACM 316. Students with credit for CMPT 464 or equivalent may not take this course for further credit.

This course will cover the fundamentals and recent advances in computer communication networks. The emphasis will be on the design and analysis of networks, especially switching, routing, and network topology.

Investigates the design and operation of the global network of networks: the Internet. This course studies the structure of the Internet and the TCP/IP protocol suit that enables it to scale to millions of hosts. The focus is on design principles, performance modelling, and services offered by the Internet.

This course covers recent developments in discrete, combinatorial, and algorithmic geometry. Emphasis is placed on both developing general geometric techniques and solving specific problems. Open problems and applications will be discussed.

Algorithm design often stresses universal approaches for general problem instances. If the instances possess a special structure, more efficient algorithms are possible. This course will examine graphs and networks with special structure, such as chordal, interval, and permutation graphs, which allows the development of efficient algorithms for hard computational problems.

This course will cover a variety of optimization models, that naturally arise in the area of management science and operations research, which can be formulated as mathematical programming problems. Equivalent Courses: CMPT860.

This course investigates the design, classification, modelling, analysis, and efficient use of communication networks such as telephone networks, interconnection networks in parallel processing systems, and special-purpose networks. Equivalent Courses: CMPT881.

Examination of the factors which influence decision making in the criminal justice system. The exercise of discretion by criminal justice personnel; the role of organizational policies and priorities in decision making; the involvement of victims and the public. Consideration of decision making at specific stages of the criminal justice process. Prerequisite: CRIM 131.

Considers the nature, extent, and basis of terrorism as an official crime throughout the world and its impact upon criminal justice systems. Theoretical explanations in a comparative perspective will be employed to examine the impact of terrorism on various countries and the response of governments to it. Prerequisite: CRIM 101.

Provides an overview of the advanced issues relating to the scientific study, development and evaluation of criminal profiling. Outlines the criminological and psychological principles upon which criminal profiling is based, including classification of violent behaviour, behavioural change and consistency. Prerequisite: CRIM 101.

Examines data handling, data quality and analysis of various criminal justice system information sources common to police services, government agencies and academic researchers. Develops skills in tactical, strategic and administrative crime analysis functionality. Prerequisite: CRIM 352 is recommended. Students who have taken CRIM 418 under this topic may not take this course for further credit.

Intensive exposure to the major streams of criminological theory, the role of theory, and how theory develops, evolves, and changes. Consideration will be given to the relationship between criminological theories, their testable hypotheses and empirical support, as well as the interplay of theory and practice. Students enrolling in this course are expected to have a solid background in undergraduate criminological theory. Equivalent to CRIM 300W.

An examination of research on crime that considers spatial and temporal dimensions. Subject matter will vary according to instructor interests and specialization. Specific areas of concentration may include: environmental criminology, communities and crime, neighborhoods and crime, ecology of crimes, and crime prevention.

An examination of contemporary issues in the context of young offenders and justice. Subject matter will vary according to instructor interest and specialization. Specific areas of concentration may include: social control theory and developmental life course theories and juvenile justice models; young offender legislation in Canada; neuropsychology and young offenders; serious and violent young offenders; and the assessment of theories of rehabilitation.

An introduction to policy analysis in the field of criminal justice, beginning with frames of reference for policy-making such as the market, welfare economics, equity, efficiency, and liberty. Through applied examples, students will define policy problems, identify goals and objectives, devise alternative solutions, predict the effects of these alternatives, and communicate advice to decision-makers.

A range of research techniques generally subsumed under the rubric of qualitative research including field research, interview techniques, historical and legal research, and documentary analysis. Emphasis will be on the logic underlying such inquiry, the advantages and limitations associated with different sources of information and procedures, and the processes by which analytical rigor is achieved. Students enrolling in this course are expected to have a solid background in undergraduate qualitative research methods, equivalent to CRIM 321.

Advanced quantitative techniques for spatial analysis of geographic data and patterns. Topics include geostatistics, spatial interpolation, autocorrelation, kriging, and their use in geographic problem solving with spatial analysis software. Prerequisite: GEOG 251 or one of STAT 101, 201, 203 (formerly 103), or 270. Quantitative.

Spatial models for the representation and simulation of physical, human and environmental processes. GIS and spatial analysis software are used in the laboratory for model development, from problem definition and solution to visualization. Prerequisite: GEOG 251 or one of STAT 101, 201, 203 (formerly 103), or 270; one of GEOG 351, 352, 353, 355 or 356. Quantitative.

Examination of advanced topics in remote sensing, including calibration /validation, spatial scale, data fusion, and the role of remote sensing in a spatial world. Students will work on independent projects applying remote sensing in their area of interest. Prerequisite: GEOG 353. Recommended: One of GEOG 351, 352, 355 or 356. Students with credit for GEOG 453W may not repeat this course for further credit. Quantitative.

A critical examination of advanced topics in GIS, such as: boundary definition, expert systems and artificial intelligence, error and uncertainty, and scale in a digital context. Examines social applications and the roles of GIS in society. Students will design original projects, including data acquisition, analysis, and web site development. Prerequisite: GEOG 355. Students with credit for GEOG 452 or GEOG 455W may not take this course for further credit. Quantitative.

The concepts, theories, and technology behind interactive and immersive interface technologies used for geospatial visualization. Applications and implications for GIScience and spatial knowledge acquisition. Combines GIScience, spatial cognition, and virtual environments/interface research perspectives. Prerequisite: GEOG 356. Students with credit for GEOG 457 (STT) Geospatial Virtual Environments in fall 2005 or fall 2006 may not take this course for further credit.

Qualitative and quantitative techniques relevant to human geographic research. Equivalent Courses: GEOG704.

Research design, data collection and quantitative methods in physical geography. Equivalent Courses: GEOG706.

Perspectives on the description, analysis and prediction of geographical processes using spatial modeling and decision-making in a GIS environment. Equivalent Courses: GEOG714.

Selected principles and applications of remote sensing for the study of natural and human environments.

Examines data, data structures and computational methods that underlie GIS description and analysis. Illustrates the social science and science links between computers and geography. Equivalent Courses: GEOG715.

The underlying concepts and methods of epidemiology in the context of population and public health. Study designs (clinical trials, cohort studies, case-control studies, and cross-sectional), measures of disease frequency and effect, validity and precision, confounding and effect modification, analysis of two-by-two tables, and options for control. Students will acquire skills in the critical interpretation of the epidemiologic literature, methodology of estimating measures of disease frequency and effect and common measures of potential impact; evaluation of study design; analysis of bias and confounding; and options for control of extraneous factors. HSCI 801 may be taken concurrently.

Methodologies and strategic research design for advances in knowledge and understanding in the health sciences. Problem definition, sampling, data collection, analysis, proposal writing, and ethical issues are addressed. Provides experiential and intellectual grounding in surveys, interviews, focus groups, and ethnography. Prerequisite: ¶¡ÏãÔ°AV to the graduate program or permission of the Instructor.

Follow-up course to HSCI 802. Designing, conducting, analyzing, and interpreting epidemiologic research. Theoretical frameworks, concepts of inference, measures of disease occurrence and effect, study designs, issues in measurement, bias, confounding, and interaction. Critical assessment of the epidemiologic and public health literature. Prerequisite: HSCI 801 and 802.

Concepts of health, illness, sickness and disease. History and development of health systems, and comparison of the social ethics, organization, and financing of different national health systems. The design of health systems - strengths and weaknesses of alternative systems for health care and delivery. Current strategies for health system reform in resource-rich and resource-constrained nations. A case studies approach. Prerequisite: ¶¡ÏãÔ°AV to the graduate program or permission of the instructor.

Practical approaches to health needs assessment, needs prioritization, health program planning, and health program evaluation in low-to-middle income countries and/or resource-poor settings. Gender-based analyses are emphasized throughout. A case study approach. Prerequisite: ¶¡ÏãÔ°AV to the graduate program or permission of the instructor.

Components of health care systems, issues, and interactions between components. System outputs, medical services and the delivery of primary health care. The Canadian health system and alternatives that impact it or provide better models for delivery. Effecting change, policy development, health system design; criteria for evaluating alternatives. Comparison of different measures of health status; trend analysis for predicting future health care and funding. Components of expenditure. Prerequisite: ¶¡ÏãÔ°AV to the graduate program or permission of the instructor.

Examination of the major social and behavioral variables -- social class, poverty, income distribution, gender, race, social networks/support, psychological stress, community cohesion, and the work and neighborhood environment -- that affect the public's health. Evaluation of the empirical research linking each construct to population health status. Methods are introduced to operationalize each construct for the purposes of application in public health research. Prerequisite: ¶¡ÏãÔ°AV to the graduate program or permission of the instructor.

Globalization and industrialization impacts on the health of the environment, populations, and workers. Environmental hazards in consumables (food, air, and water) and waste (liquid, solid, and gaseous) with special reference to hazardous waste. Risk assessment in community, workplace, and residential settings. A case studies approach. Prerequisite: ¶¡ÏãÔ°AV to the graduate program or permission of the instructor.

A presentation of the problems commonly arising in numerical analysis and scientific computing and the basic methods for their solutions. Prerequisite: MATH 152 or 155 or 158, and MATH 232 or 240, and computing experience. Quantitative.

Development of numerical methods for solving linear algebra problems at the heart of many scientific computing problems. Mathematical foundations for the use, implementation and analysis of the algorithms used for solving many optimization problems and differential equations. Prerequisite: MATH 251, MACM 316, programming experience. Quantitative.

The numerical solution of ordinary differential equations and elliptic, hyperbolic and parabolic partial differential equations will be considered. Prerequisite: MATH 310 and MACM 316. Quantitative.

Linear programming modelling. The simplex method and its variants. Duality theory. Post-optimality analysis. Applications and software. Additional topics may include: game theory, network simplex algorithm, and convex sets. Prerequisite: MATH 150, 151, 154, or 157 and MATH 240 or 232. Quantitative.

Theoretical and computational methods for investigating the minimum of a function of several real variables with and without inequality constraints. Applications to operations research, model fitting, and economic theory. Prerequisite: MATH 232 or 240, and 251. Quantitative.

Inventory theory, Markov decision process and applications, queuing theory, forecasting models, decision Analysis and games, probabilistic dynamic programming, simulation modeling, project planning using PERT/CPM, sequencing and scheduling. Prerequisite: STAT 270. Pre-/Co-requisite: MATH 308. Quantitative.

Model building using integer variables, computer solution, relaxations and lower bounds, heuristics and upper bounds, branch and bound algorithms, cutting plane algorithms, valid inequalities and facets, branch and cut algorithms, Lagrangian duality, column generation of algorithms, heuristics algorithms and analysis. Prerequisite: MATH 308. Quantitative.

Graph coloring, Hamiltonian graphs, planar graphs, random graphs, Ramsey theory, extremal problems, additional topics. Prerequisite: MATH 345. Quantitative.

Applications of network flow models; flow decomposition; polynomial algorithms for shortest paths, maximum flows and minimum costs flows; convex cost flows; generalized flows, multi-commodity flows. Prerequisite: MATH 308. Recommended: MATH 345. Quantitative.

Held jointly with MATH 408-3. See description for MATH 408-3. Students may not take a 700 division course if it is being offered in conjunction with a 400 division course which they have taken previously.

Held jointly with MACM 409-3. See description for MACM 409-3. Students may not take a 700 division course if it is being offered in conjunction with a 400 division course which they have taken previously.

The numerical solution of ordinary differential equations and elliptic, hyperbolic and parabolic partial differential equations will be considered. Students may not take a 700 division course if it is being offered in conjunction with a 400 division course which they have taken previously.

Graph coloring, Hamiltonian graphs, planar graphs, random graphs, Ramsey theory, extremal problems, additional topics. Students may not take a 700 division course if it is being offered in conjunction with a 400 division course which they have taken previously.

Held jointly with MATH 448-3. See description for MATH 448-3. Students may not take a 700 division course if it is being offered in conjunction with a 400 division course which they have taken previously.

The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in experimental research. Prerequisite: Any STAT course (except STAT 100), or BUEC 232, or ARCH 376. Statistics major and honors students may not use this course to satisfy the required number of elective units of upper division statistics. However, they may include the course to satisfy the total number of required units of upper division credit. Quantitative.

Theory and application of linear regression. Normal distribution theory. Hypothesis tests and confidence intervals. Model selection. Model diagnostics. Introduction to weighted least squares and generalized linear models. Prerequisite: STAT 285, MATH 251, and one of MATH 232 or MATH 240. Quantitative.

A practical introduction to useful sampling techniques and intermediate level experimental designs. Statistics major and honors students may not use this course to satisfy the required number of elective units of upper division Statistics. However, they may include the course to satisfy the total number of required units of upper division credit. Prerequisite: STAT 302, 305 or 350 or BUEC 333. Students with credit for STAT 410 or 430 may not take STAT 403 for further credit. Quantitative.

An introduction to the major sample survey designs and their mathematical justification. Associated statistical analyses. Prerequisite: STAT 350. Quantitative.

An extension of the designs discussed in STAT 350 to include more than one blocking variable, incomplete block designs, fractional factorial designs, and response surface methods. Prerequisite: STAT 350 (or MATH 372). Quantitative.

The use of statistical techniques and mathematical models in resource management with special emphasis on experimentation, survey techniques, and statistical model construction. This course may not be used for the satisfaction of degree requirements in the Department of Statistics and Actuarial Science. Prerequisite: A course in parametric and non-parametric statistics.

Application of stochastic processes: queues, inventories, counters, etc. Reliability and life testing. Point processes. Simulation. Students with credit for STAT 870 may not take this course for further credit.

Statistical methodology used in analysing failure time data. Likelihoods under various censoring patterns. Inference using parametric regression models including the exponential, Weibull, lognormal, generalized gamma distributions. Goodness-of-fit tests. The proportional hazards family, and inference under the proportional hazards model. Stratification and blocking in proportional hazards models. Time dependent covariates. Regression methods for grouped data. Prerequisite: STAT 450. Students with credit for STAT 806 may not take this course for further credit.