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Instructors:
Class Schedule: · Tuesday 11:45am-1:25pm; Thursday 2:50-4:30pm EST · Location: Remote on Zoom (See Canvas for link) Office Hours: · Alina: o Tuesday, 4:00-5:30pm EST, Remote on Zoom o Thursday, 4:30-5:30pm EST, Remote on Zoom · Alex: Wednesday 5:00-7:00pm EST · Matthew: Monday 3:00-4:00pm, Friday 9:00-10:00am EST Class forum: Piazza (See Canvas for link) Class policies: Academic integrity
policy is strictly enforced. Class description: Machine
learning is a fast-pacing and exciting field achieving human-level
performance in tasks such as image classification, speech recognition.
machine translation, precision medicine, and self-driving cars. Machine
learning has already impacted greatly our daily lives and has the potential
to transform the world even more in the near future. This course will provide a broad introduction to machine
learning and cover the fundamental algorithms for supervised learning. We
will cover topics related to regression, linear classification, non-linear
classification, ensemble models, and deep learning. The class will also
provide an introduction into ethics and fairness concerns of machine
learning, as well as adversarial machine learning, an emerging area that
studies the fundamental security issues of machine learning. Pre-requisites: · Probability · Statistics · Linear algebra Textbook
[ISL] Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. Grading
The
grade will be based on: - Assignments – 25% - Final project report and presentation – 35% - Exam – 35% - Class participation – 5% |
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