How do computers understand language? It seems impossible that zeroes and ones could ever add up to words that humans can understand, but machine language has come a long way in the past few years. Let us take you behind the code to explain how machines simulate language comprehension, and why it’s a far more complicated problem than “bonjour = hello.” This talk is aimed at an audience that is not necessarily familiar with computers or language comprehension but would like a primer to the field, and what it can realistically do.

The UPEI Department of Music is pleased to present the 2024 Dr. Frances Gray Performance Competition, featuring five finalists selected in a preliminary round:

Devyn Collette, alto saxophone
Dre Knowles, piano
Timothy Luk, piano
Janey-Lynn Perrier, soprano
Jacob Reddin, trombone

Have you wondered how machine learning models can suddenly do so many different types of work? How is it that machines can learn things like language, vision, and translation in such a short amount of time, and what has helped drive these kinds of improvements? The obvious answers--big data and big processors--are only part of the story, and to understand the full picture, we need to take a closer look at the models driving the AI revolution.

A great deal of high-performance computing software is written in C++, but few universities offer courses in the language any more. If you have to work with "legacy code" written in C++, adding features, porting to a new machine, or patching errors, or if you need to extend packages like OpenFOAM which are written in C++, then this workshop is for you.

C++ was designed as an extension of the C language but has its own distinct idiom or style. This workshop assumes that you already know C to the level reached in the ACENET workshop, "C as a Second Language."

Are you curious about machine learning, but not sure where to start, or if the discipline is for you? Join ACENET for a survey and explanation of several methods used to make machines learn--from simple models like Naive Bayes, Regression, and Decision Trees to an introduction to Support Vector Machines and Feed-Forward Neural Networks.

A great deal of high-performance computing software is written in C, but few universities offer courses in the language any more. If you have to work with "legacy code" written in C, adding features, porting to a new machine, or patching errors, or if you need to write user-defined functions for engineering packages such as Fluent, then this workshop is for you.

Prerequisites: Familiarity with some other programming language.

How can you make smart decisions about setup and execution of a machine learning project? How should you hire and support the staff working on the project? We hope to provide clear, thoughtful answers to these and other common questions to get you thinking about whether machine learning is a technology that you and your company or group should think about investing in.

This conference is a free, one-day virtual career sonference for 100 university and college students from across Canada facing one or more barriers* in the workforce, to provide professional skill development, real-world education from diverse industry professionals, and opportunities for students to network with companies they might not otherwise have the chance to!

*Our list of eligible barriers includes but is not limited to race, gender, sexual orientation, and physical ability; a full list of eligibility can be found within the application.
 

Fortran, one of the initial high-level programming languages, continues to be an excellent option for high-performance computing due to its superb performance. The newer versions offer many modern features, including object-oriented programming capabilities to programmers. This course will cover some of these features.

Prerequisites: Familiarity with another programming language.

The session will take place Wednesday, April 24, 10:00 am--12:00 and 2:00--4:00 pm.