Workshop: Machine Learning in Python - Session 1
Machine Learning in Python - ML overview and k-nearest neighbours algorithm
翱惫别谤惫颈别飞:听Nowadays, machine learning (ML) is perhaps the hottest topic in all Computer Science, and with good reason: the variety of tasks that can be completed by machine learning models has exploded in the last 15 years as compute power has reached new heights. But what exactly is a 鈥渕achine learning model鈥�? 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.
Learning Goal(s): By the end of the workshop, participants will be able to:
- Describe at a high level what machine learning is and how it works, the uses and applications of machine learning, as well as its limitations and ethical considerations.
- Describe the machine learning pipeline, consisting of data acquisition, data cleaning, algorithm selection, training, testing, and validation.
- Explain in plain English how the following algorithm works: k-nearest neighbours
Prereqs: Participants should already have some familiarity with Python programming fundamentals, e.g. loops, conditional execution, importing modules, and calling functions.
Approach: Our approach is primarily student-centered. Students will work in pairs and small groups on worksheets and Jupyter notebooks, interspersed with brief lecture and instructor-led live-coding segments.
Supporting Resources: We will refer to many of the materials used previously in our series 鈥淐omputing Workshop鈥�
Deliverables: Our resources will be made available via our web site.
Resources required: Participants should have access to a laptop computer. Python should be already installed with Anaconda.
Location:聽HYBRID at the聽,听room 325, and via Zoom.
滨苍蝉迟谤耻肠迟辞谤蝉:听, Faculty Lecturer in Computer Science at 捆绑SM社区. Eric Mayhew, Computer Science professor at Dawson College.
Registration:
