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Instructors:
Class Schedule: · Tuesday 11:45am-1:25pm; Thursday 2:50-4:30pm · Location: Office Hours: · Alina: Thursday, 4:30-6:00pm, ISEC 625 · Ewen: Monday, 5:30-6:30pm, ISEC TBD Class forum: Piazza 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 and unsupervised learning. We will cover topics
related to regression, classification, deep learning, dimensionality reduction,
and clustering. The class will also provide an introduction into 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. [PDF] Grading
The grade will be based on: -
Assignments –
25% -
Final project
report and presentation – 35% -
Exam –
35% -
Class
participation – 5% |
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Books:
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