Forms of cultural expression in Spain, Latin America and Spanish-speaking North America, with study of representative media, including literature, journalism, film, visual art, and the urban environment. Introduction to methods of cultural analysis.

A survey of Latin American cinema, analyzed within historical, social, political, and cultural contexts. Aesthetic and social forms and questions of identity will also be studied. Throughout the course, the cinema of various Spanish speaking nations, regions, and historical periods will be highlighted. The course is taught in English. Students who take this course for Spanish Language Citation must complete written course work in Spanish.

An intermediate level Spanish course focusing on topics and language related to professional and media spheres. Students will work with language appropriate for the workplace, newsprint, and online media, as well as financial and diplomatic institutions. Topics studied will include foreign affairs, business, advertisement, and the geopolitics of Spain, Latin and North America. Students will gain literacy and intercultural skills, as well as critical thinking skills through the study of workplace culture, newsprint and social media, current and historical political events. Writing practice may include letters, CVs, opinion pieces, as well as social media posts.

This course is designed for heritage and native speakers with solid reading and writing abilities in Spanish as well as fluent speaking and listening skills. The course provides opportunities to develop a complex Spanish grammatical system as well as opportunities to increase vocabulary, and develop writing skills and the ability to use the language across different contexts including in more formal situations.

Advanced Spanish for non-natives. Selective review of grammar with emphasis on the complex sentence; intensive practice in written and oral expression to improve proficiency. As part of this course, students may have the option of participating in an international learning experience that will have an additional cost and application process.

Practical uses of spoken and written Spanish for business contexts. This course builds on grammar and vocabulary knowledge already acquired at the intermediate level.

The course introduces students to the different ways in which speakers communicate across the diverse Spanish-speaking world. Students will analyze how culturally defined politeness, social norms, and speaker identities impact how language is used in diverse Spanish-speaking countries. Students will also explore, identify, and solve problems resulting from cultural differences between Spanish and other languages. Furthermore, students will develop pragmatic competence in Spanish and will compare variation in pragmatic norms among different Spanish-speaking communities.

Study of traditional topics of Spanish grammar from a linguistic perspective with the goal of improving students’ knowledge and usage of Spanish grammar and language understanding in general. Topics include (but are not limited to): word order variation patterns, subject types, the verbal system, and the Spanish copulas. This course employs a cross-linguistic approach, as some of these topics are discussed from a comparative perspective. This course is taught in Spanish.

This course introduces students to the discipline of linguistics through a focus on Spanish- speaking communities and the linguistic diversity amongst them. This advanced task-based course focuses on developing students’ pragmatic competence in Spanish, or the ability to use the language appropriately in different social contexts. To do so, students will analyze how culturally confined politeness norms, contextual elements, and speakers’ identities impact how language is used to carry out different speech acts such as requests, invitations, and apologies, among others, and will compare variation in pragmatic norms among different Spanish-speaking communities.

Introduction to the theory of probability, with emphasis on the construction of discrete probability models for applications. After this course, students are expected to understand the concept of randomness and aspects of its mathematical representation. Topics include random variables, Venn diagrams, discrete probability distributions, expectation and variance, independence, conditional probability, applications such as queues.

An introductory course in statistical concepts and methods, emphasizing exploratory data analysis for univariate and bivariate data, sampling and experimental designs, basis probability models, estimation and tests of hypothesis in one-sample and comparative two-sample studies. A statistical computing package is used but no prior computing experience is assumed.

A sequel to STA220H5, emphasizing major methods of data analysis such as analysis of variance for one factor and multiple factor designs, regression models, categorical and non-parametric methods.

This course covers probability including its role in statistical and computational modeling. Topics include classical and computational perspectives on cumulative, mass and distribution functions, random variables, expectation, limiting results, the normal distribution. Computational topics include generating and sampling random numbers, combinatorial objects and probability functions for simulation and statistical analysis. Additional techniques include resampling, hypothesis testing, model fit and cross validation. IMPORTANT NOTE: STA246H5 will not be permitted as a pre-requisite for any other 200+ level STA courses. In addition, STA246H5 cannot count towards any program(s) in Mathematics or Applied Statistics. The course is intended only for students in Computer Science programs who will not need STA256H5 for other program requirements.

This course covers probability including its role in statistical modeling. Topics include probability distributions, expectation, discrete and continuous random variables and vectors, distribution functions, distributions of functions of random variables, limit theorems, the central limit theorem.

A survey of statistical methodology with emphasis on the relationship between data analysis and probability theory. Topics covered include descriptive statistics, limit theorems, sampling distribution, point and interval estimation, hypothesis testing, contingency tables and count data, simple linear regression. A statistical computer package will be used.

A sequel to STA256H5 introducing current statistical theory and methodology. Topics include: Sampling distributions, point estimation, confidence intervals, testing (Neyman-Pearson Theorem, uniformly most powerful test, likelihood ratio tests), unbiasedness, consistency, sufficiency, complete statistics, and exponential family; Fisher Information and the Cramer-Rao inequality; simple linear models.

Analysis of the multiple regression model by least squares; statistical properties of the least square analysis, including estimation of error; residual and regression sums of squares; distribution theory under normality of the observations; confidence regions and intervals; tests for normality; variance stabilizing transformations, multicolinearity, variable search methods.

The sample survey is a widely used technique for obtaining information about a large population at relatively small cost. Only probability samples can provide both an estimator and a measure of sampling error from the data itself. In addition to sampling error, non-sampling errors (refusals, not-at-home, lies, inaccuracies, etc.) are always present, and can produce serious biases. The course covers: design of surveys, sources of bias, randomized response surveys. Techniques of sampling; stratification, clustering, unequal probability selection. Sampling inference, estimates of population mean and variances, ratio estimation, observational data; correlation vs. causation, missing data, sources of bias.

This course covers topics in the design and analysis of experiments. The topics covered include analysis of variance, randomization, confounding, block designs, factorial designs, orthogonal polynomials and response surface methods. Applications include agricultural experiments, laboratory experiments, and industrial experiments, including quality control techniques.

Introduction to a topic of current interest in statistics. Content will vary from year to year. Computer packages are used. The contact hours for this course may vary in terms of contact type (L, T) from year to year, but will be between 36-48 contact hours in total. See the UTM Timetable.

Introduction to a topic of current interest in statistics. Content will vary from year to year. Computer packages are used. The contact hours for this course may vary in terms of contact type (L, T) from year to year, but will be between 36-48 contact hours in total. See the UTM Timetable.

A thorough introduction to the basic ideas in supervised statistical learning with a focus on regression and a brief introduction to classification. Methods covered will include multiple linear regression and its extensions, k-nn regression, variable selection and regularization via AIC,BIC, Ridge and lasso penalties, non-parametric methods including basis expansions, local regression and splines, generalized additive models, tree-based methods, bagging, boosting and random forests. Content will be discussed from a statistical angle, putting emphasis on uncertainty quantification and the impact of randomness in the data on the outcome of any learning procedure. A detailed discussion of the main statistical ideas behind crossvalidation, sample splitting and re-sampling methods will be given. Throughout the course, R will be used as software, a brief introduction will be given in the beginning.

The second part of the course will focus on basic ideas in classification problems including discriminant analysis and support vector machine, and unsupervised learning techniques such as clustering, principal component analysis, independent component analysis and multidimensional scaling. The course will also cover the modern statistics in the "big data" area. The high dimensional problems when p >> n and n >> p will be introduced. In addition, the students will be formed as groups to do data analysis projects on statistical machine learning and present their findings in class. This will prepare them for future careers in industry or academia.

Discrete Markov chains with a finite number of states, random walks, single-server queues, continuous-time Markov chains, Poisson processes, branching processes, birth and death process, M/M/n queues, Monte-Carlo simulation may be introduced.

A thorough introduction to statistics from a Bayesian perspective. Methods covered will include: the rules of probability, including joint, marginal, and conditional probability; discrete and continuous random variables; discrete and continuous random variables; Bayesian inferences for means and proportions; the simple linear regression model analyzed in a Bayesian manner; and (time permitting) a brief introduction to numerical methods such as the Gibbs sampler. Throughout the course, R will be used as software, a brief introduction will be given in the beginning.

Students explore a topic in statistics under the supervision of a faculty member. Interested students must consult with statistics faculty at least two months prior to registration, to determine the topic and scope.

Computational methods play a central role in modern statistics and machine learning. This course aims to give an overview of some of the computational techniques that are useful in statistics. Topics include methods of generating random variables, Monte Carlo integration and variance reduction, Monte Carlo methods in inference, bootstrap and jackknife, resampling application, permutation tests, probability density estimation, and optimization.

Introduction to a topic of current interest in statistics. Content will vary from year to year. The contact hours for this course may vary in terms of contact type (L, T) from year to year, but will be between 36-48 contact hours in total. See the UTM Timetable.

Students explore a topic in statistics under the supervision of a faculty member. Interested students must consult with statistics faculty at least two months prior to registration, to determine the topic and scope.

This course provides a richly rewarding opportunity for students in their third or fourth year to work in the research project of a professor in return for 399H course credit. Students enrolled have an opportunity to become involved in original research, learn research methods and share in the excitement and discovery of acquiring new knowledge. Participating faculty members post their project descriptions for the following summer and fall/winter sessions in early February and students are invited to apply in early March. See Research Opportunity Program (ROP) for more details.