Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer.
Prerequisites: Some Linux command line experience. Python experience is strongly advised.
Format: Virtual
This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.10; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Lectures will be 11:00am - 12:00pm, Tuesday and Thursdays. There will be no lectures on May 30 and June 1.
Format: In-person, in the SciNet teaching room (661 University Ave., suite 1140A). All lectures will be recorded.
Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer.
Format: In-person, but will also be broadcast and recorded.
This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts.
Classes will be held, April 23 - May 30, Tuesdays and Thursdays, 11:00am - 12:00pm.
Format: Virtual
This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.9; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Lectures will be 11:00am - 12:00pm, Tuesday and Thursdays.
This workshop will provide an introduction to some of the key methods and concepts in machine learning. We will present and discuss the following topics: - Introduction to machine learning. - Regression. - Bias-variance tradeoff. - Resampling Methods. - Classification algorithms, in general. - Decision trees, kNN, k-means. - Agglomerative clustering.
We will use Python 3, and attendees are expected to be familiar with the Python programming language but extensive programming experience is not required - we will mainly be calling functions in existing packages, not writing large amounts of code.