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Instructors:
Class Schedule:
Office Hours:
Office hours held virtually on the Khoury Office Hours Application. If you do not have a Khoury account please apply for one here. Class forum: Piazza 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 generative AI, such as Large Language Models (LLMs). Pre-requisites:
Textbook
[ISL] Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. An Introduction to Statistical Learning with Applications in Python. Labs for ISL textbook available on Github. Grading
The grade will be based on: - Assignments – 25% - Final Project – 30% - Midterm Exam – 20% - Final Exam – 20% - Class Participation – 5% All students have five late days to use, free of penalty, across the four assignments. After the five late days have been used, assignments will incur a 20% penalty per late day. Students are asked to keep track of late days themselves; however, please email if you are unsure of the number of late days remaining. |
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Calendar (Tentative) |
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Slides will be posted online shortly after in-class
lecture.
Books:
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