In person registration (for online, please fill in this form instead)
Date: Friday, 17 February 2023.
Time: 10 a.m. to 12 p.m.
Location: room 1104 (11th floor).
滨苍蝉迟谤耻肠迟辞谤:听Jacob Errington, Faculty Lecturer, and Eric Mayhew, graduate student, School of Computer Science, 香蕉视频.
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Overview: Nowadays, machine learning (ML) is perhaps the hottest topic in all Computer Science, and with good reason: the variety of tasks that machine learning models can complete has exploded in the last 15 years as computing power has reached new heights. But what exactly is a 鈥渕achine learning algorithm鈥�? And at what cost do these advances in computing have for society and the environment? This workshop will introduce you to the basic terminology and concepts associated with machine learning in a hands-on way. We will explore common ML tasks such as data acquisition and cleaning as well as model training, testing, and validation by focusing on a particularly simple kind of model called k-nearest neighbours.
This workshop marks the first in the series we will teach. It provides the necessary background for the subsequent sessions, each of which is standalone. Students with some background in machine learning can skip this session and sign up for the following ones that interest them. We especially encourage beginners to sign up for this session in conjunction with several of the following ones.
By the end of the workshop, you will be able to:
> Articulate applications, limitations, and ethical considerations of machine learning;
> Enumerate the machine learning pipeline: data acquisition, data cleaning, algorithm selection, training, testing, and validation;
> Explain in plain English how the following algorithm works: k-nearest neighbours;
Prerequisites:
路 Participants should already have some familiarity with Python programming fundamentals, most importantly importing modules and calling functions. Jupyter notebooks will be used.
路 You need to bring your own laptop for this workshop. Contact us if you would like to attend but it's impossible for you to bring a laptop.
路 Install Anaconda on your computer. You can find installation instructions here. Please contact us (cdsi.science [at] mcgill.ca) if you are having trouble with installation.
Supporting resources: Some materials that will be used are available at the instructors' website .
