Johann Thiel’s Profile Faculty Toggle navigation

The study of con­tin­u­ous-time and dis­crete-time non­lin­ear dy­namic mod­els. Pro­vides ex­am­ples of chaotic be­hav­ior of so­lu­tions of some dy­namic sys­tems.

The study of continuous-time and discrete-time nonlinear dynamic models. Provides examples of chaotic behavior of solutions of some dynamic systems.

This course in­tro­duces the foun­da­tions of dis­crete math­e­mat­ics as they apply to com­puter sci­ence, fo­cus­ing on pro­vid­ing a solid the­o­ret­i­cal foun­da­tion for fur­ther work. Top­ics in­clude func­tions, re­la­tions, sets, sim­ple proof tech­niques, Boolean al­ge­bra, propo­si­tional logic, el­e­men­tary num­ber the­ory, writ­ing, an­a­lyz­ing and test­ing al­go­rithms.

This course introduces the foundations of discrete mathematics as they apply to computer science, focusing on providing a solid theoretical foundation for further work. Topics include functions, relations, sets, simple proof techniques, Boolean algebra, propositional logic, elementary number theory, writing, analyzing and testing algorithms.

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.

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.

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.