Mathematics

The International Internship course develops vital business skills employers are actively seeking in job candidates. This course is comprised of two parts: an internship, and a hybrid academic seminar. Students are placed in an internship within a sector related to their professional ambitions. The hybrid academic seminar, conducted both online and in-person, analyzes and evaluates the workplace culture and the daily working environment students experience. The course is divided into eight career readiness competency modules as set out by the National Association of Colleges and Employers (NACE), which guide the course’s learning objectives. During the academic seminar, students reflect weekly on their internship experience within the context of their host culture by comparing and contrasting their experiences with their global internship placement with that of their home culture. Students reflect on their experiences in their internship, the role they have played in the evolution of their experience in their internship placement, and the experiences of their peers in their internship placements. Students develop a greater awareness of their strengths relative to the career readiness competencies, the subtleties and complexities of integrating into a cross-cultural work environment, and how to build and maintain a career search portfolio.

This course teaches data-based model inference and predictive model generation. It covers the core principles of the question structure, data collection and organization, statistical inference, predictive modeling, and decision-making process. The course also studies basic theories about intermediate-level data conversion, data refinement, model fit, model selection, model diagnosis, etc., and learn them by data practice.

This course offers a study of topology. Topics include: metric spaces; topological spaces; continuous application; separation properties; compactness; locally compact spaces and compactifications; connection and paths.

This course provides an introduction to artificial neural networks and deep learning, with both theoretical and practical aspects. This course gives a basic knowledge of artificial neural networks and deep learning: both the theoretical background and how to practically use these methods for typical problems in machine learning and data mining. The course covers the most common models in artificial neural networks, with a focus on the multi-layer perceptron. The course contains three computer exercises where the student train and evaluate different ANN models.

 This course offers an introduction to Markov processes in discrete and continuous time. Topics include Markov chains, Poisson process, Markov processes, and an introduction to renewal theory and regenerative processes.

This course examines the fundamentals of Bayesian inference, including the specification of prior and posterior distributions, Bayesian decision theoretic concepts, the ideas behind Bayesian hypothesis tests, model choice and model averaging, the capabilities of several common model types, such as hierarchical and mixture models. It also looks at the ideas behind Monte Carlo integration, importance sampling, rejection sampling, Markov chain Monte Carlo samplers such as the Gibbs sampler and the Metropolis-Hastings algorithm, and use of the WinBuGS posterior simulation software.