Calendar

DESCRIPTION: This lecture introduces the fundamental concepts of an associative network in neural computation. Studying a simple network architecture allows analyzing the process of associating one memory to another through tuned synaptic connections. The discussion combines mathematical and computational study of this system, setting the foundation for further study in neural networks and machine learning. This course will be 50% lecture and 50% lab. The lab will be hands-on, with students able to work interactively at the computer they use for the Zoom session.

TEACHERS: Lyle Muller (Western University, OBI Centre for Analytics) and Roberto Budzinski (University of Lethbridge, OBI Centre for Analytics)

LEVEL: Intermediate

FORMAT: Lecture + Hands-on.

CERTIFICATES: Attendance and Completion

PREREQUISITES:

* Basic linear algebra (vectors, matrices, matrix multiplication) and programming (functions, variables, loops).
* Basic Python knowledge and know-how.

NOTE: THIS COURSE HAS LIMITED ENROLMENT. IF ENROLLED AND YOU WILL NOT BE ABLE TO ATTEND, THEN KINDLY UNENROL SO ANOTHER PERSON CAN ENROL.

DESCRIPTION: Are you not sure which workshops to sign up for in this Summer School? In this session, we will give an overview of the program of the Compute Ontario Summer School to help you decide. We'll also show you what other training opportunity in Advanced Research Computing and Research Data Management are available for you in Canada after the summer school.

TEACHER: Ramses van Zon (SciNet, University of Toronto)

LEVEL: Introductory

FORMAT: Webinar

CERTIFICATE: Attendance

PREREQUISITES: None

DESCRIPTION: Jupyter Notebook is commonly used for interactive computing in Python. This session provides the options and features for working with Jupyter on the Digital Research Alliance of Canada's remote computing clusters and demonstrates several use case examples on the clusters.

TEACHER: Jinhui Qin (SHARCNET, Western University)

LEVEL: Introductory

FORMAT: Lecture + Demonstration

CERTIFICATE: Attendance

PREREQUISITES: Basic Python and Linux command line experience.

DESCRIPTION: This lecture introduces the fundamental concepts of an associative network in neural computation. Studying a simple network architecture allows analyzing the process of associating one memory to another through tuned synaptic connections. The discussion combines mathematical and computational study of this system, setting the foundation for further study in neural networks and machine learning. This course will be 50% lecture and 50% lab. The lab will be hands-on, with students able to work interactively at the computer they use for the Zoom session.

TEACHERS: Lyle Muller (Western University, OBI Centre for Analytics) and Roberto Budzinski (University of Lethbridge, OBI Centre for Analytics)

LEVEL: Intermediate

FORMAT: Lecture + Hands-on.

CERTIFICATES: Attendance and Completion

PREREQUISITES:

* Basic linear algebra (vectors, matrices, matrix multiplication) and programming (functions, variables, loops).
* Basic Python knowledge and know-how.

NOTE: THIS COURSE HAS LIMITED ENROLMENT. IF ENROLLED AND YOU WILL NOT BE ABLE TO ATTEND, THEN KINDLY UNENROL SO ANOTHER PERSON CAN ENROL.

DESCRIPTION: Using version control for your scripts, codes, documents, papers, and even data, allows you to track changes, keep backups, and facilitate collaboration. This introductory workshop will teach you the basics of version control with the popular distributed version control software GIT. This workshop assumes that students have a basic understanding of Linux shell commands.

TEACHER: James Willis (SciNet, University of Toronto)

LEVEL: Introductory

FORMAT: Lecture + Hands-on

CERTIFICATE: Attendance

PREREQUISITE: Basic understanding of Linux shell commands.