Bao’s Profile Student Toggle navigation

A pro­ject-based in­tro­duc­tion to com­pu­ta­tional think­ing and prob­lem solv­ing. Cov­ers a wide range of top­ics, in­clud­ing data vi­su­al­iza­tion, sta­tis­ti­cal tech­niques, sim­u­la­tions of dy­nam­i­cal sys­tems, com­pu­ta­tional tech­niques to un­der­stand data, using re­gres­sion to fit mod­els to data, as well as an in­tro­duc­tion to some more ad­vanced top­ics: Monte Carlo sim­u­la­tions, op­ti­miza­tion, dy­namic pro­gram­ming, image pro­cess­ing, nat­ural lan­guage pro­cess­ing, geospa­tial data analy­sis and cur­rent data sci­ence.

A project-based introduction to computational thinking and problem solving. Covers a wide range of topics, including data visualization, statistical techniques, simulations of dynamical systems, computational techniques to understand data, using regression to fit models to data, as well as an introduction to some more advanced topics: Monte Carlo simulations, optimization, dynamic programming, image processing, natural language processing, geospatial data analysis and current data science.

An in­tro­duc­tion to solv­ing or­di­nary dif­fer­en­tial equa­tions. Ap­pli­ca­tions to var­i­ous prob­lems are dis­cussed.

An introduction to solving ordinary differential equations. Applications to various problems are discussed.

This course con­tin­ues the dis­cus­sion of dis­crete math­e­mat­i­cal struc­tures and al­go­rithms in­tro­duced in MAT2440. Top­ics in the sec­ond course in­clude pred­i­cate logic, re­cur­rence re­la­tions, graphs, trees, dig­i­tal logic, com­pu­ta­tional com­plex­ity and el­e­men­tary com­putabil­ity.

This course continues the discussion of discrete mathematical structures and algorithms introduced in MAT2440. Topics in the second course include predicate logic, recurrence relations, graphs, trees, digital logic, computational complexity and elementary computability.

The use of dis­crete and con­tin­u­ous dis­tri­b­u­tions to con­struct de­ter­min­is­tic and sto­chas­tic sim­u­la­tion mod­els. Sto­chas­tic sim­u­la­tions may in­clude Markov Processes, M/G/1 Queu­ing Sys­tems, Monte Carlo Sim­u­la­tion and An­a­lytic Sim­u­la­tion.

The use of discrete and continuous distributions to construct deterministic and stochastic simulation models. Stochastic simulations may include Markov Processes, M/G/1 Queuing Systems, Monte Carlo Simulation and Analytic Simulation.

A 3 credit but 4 hour in­tro­duc­tory course. Top­ics in­clude sam­ple space, ex­pec­ta­tion and vari­ance, bi­no­mial, Pois­son, nor­mal, stu­dent and chi-square dis­tri­b­u­tions, con­fi­dence in­ter­val, hy­poth­e­sis test­ing, cor­re­la­tion and re­gres­sion. Stu­dents do a group pro­ject com­par­ing 2 nu­meric vari­ables and pre­sent at the end of the se­mes­ter. The extra class­room hour is de­signed to fa­cil­i­tate a hands-on feel to the course in­clud­ing a heavy use of MS Excel and/or R.

A 3 credit but 4 hour introductory course. Topics include sample space, expectation and variance, binomial, Poisson, normal, student and chi-square distributions, confidence interval, hypothesis testing, correlation and regression. Students do a group project comparing 2 numeric variables and present at the end of the semester. The extra classroom hour is designed to facilitate a hands-on feel to the course including a heavy use of MS Excel and/or R.