Computer Science and Math talks

Nipissing University’s Computer Science and Math department is pleased to offer a series of lectures aimed at high school students and a general audience.First on the agenda is Dr. Alex Karassev, discussing Graphs and Surfaces on November 22, from 1:30 – 2:30 p.m. in room A223.
Here’s an abstract:
We will introduce the audience to the theory of graphs and surfaces and will discuss classical results about embedding of graphs into plane and surfaces, graph colorings, classification of surfaces, and invariants. These topics have far-reaching generalizations in modern geometric topology.Future lectures are as follows:February 21: Dr. Haibin ZhuTitle: Overview of Computer Science and Collaborative Systems
Abstract: We will talk about the learning goals, major topics in computer science, current research topics and challenges, and applications of computers. We will also answers the following questions: Why do students choose computer science as their field? What are the career paths that they pursue with their CSc degrees? We also discuss collaborative systems including problems, processes, challenges, models, methodologies, and applications.March 21: Dr. Boguslaw SchreyerTitle: Artificial Neural Networks
Abstract: In computer science and related fields, artificial neural networks are computational models inspired by animal central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected "neurons" that can compute values from inputs by feeding information through the network.
For example, in a neural network for handwriting recognition, a set of input neurons may be activated by the pixels of an input image representing a letter or digit. The activations of these neurons are then passed on, weighted and transformed by some function determined by the network's designer, to other neurons, etc., until finally an output neuron is activated that determines which character was read.
Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. An example of pattern recognition will be also presented.April 11: Dr. Vesko ValovTitle: Impossible constructions
Abstract: We will talk about the three most famous geometric constructions, impossible to perform using compass and straightedge. The problems of squaring the circle, doubling the cube, and angle trisection have been known for over 2000 years, but impossibility of these constructions have been completely proved only in 19th century. Attempts to solve these problems led to significant discoveries in geometry, algebra, and number theory.