Instructors:
Class Schedule: · Tuesday 11:45am1:25pm; Thursday 2:504:30pm · Location: Ryder Hall 158 Office Hours: · Alina: Thursday, 4:306:00pm, ISEC 625 · Anand: Tuesday, 23pm, ISEC 605 Class forum: Piazza Class description: Machine
learning is a fastpacing and exciting field achieving humanlevel
performance in tasks such as image classification, speech recognition.
machine translation, precision medicine, and selfdriving 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, Prerequisites: · 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 – 20%  Final project report and presentation – 25%  Midterm exam – 25%  Final exam – 25%  Class participation – 5% 

Calendar (Tentative) 

Books:




