COS424/SML302: Fundamentals of Machine Learning
Tuesday and Thursday 11:00AM-12:20PM
Friend Center 101
Friday 11:00AM-12:20PM and 1:30PM-2:50PM
Friend Center 101 and Computer Science 105
Office: Computer Science 322
Hours: Tuesday 2:30-3:30PM; COS 302
Office: Room 104 at 221 Nassau St.
For current hours and locations, see Piazza website.
Description, syllabus, and readings
Administrative To Do
Sign up on
Take the seven minute
! We will use these data in our in-class examples.
All course materials, demos, homeworks, and project descriptions will be posted on the
Piazza course website
Python coding and machine learning:
includes many python packages for a large range of machine learning methods and models.
is a simple data analysis tool for working with data in a reproducible way.
Here are some resources for learning and using R:
Download R at the
R Project for Statistical Computing
Start to learn R by reading
Introductory Statistics with R
by Peter Dalgaard (Ch 1-2).
Many people like
Some people use
Emacs Speaks Statistics
for beautiful graphics and figures.
for reproducible R pipelines.
Additional books and reading that you might find useful:
The Hastie et al. book,
Elements of Statistical Learning
can be found
Introduction to Statistical Thought
(an introductory statistical textbook with plenty of R examples, and it's online too)
David J.C. MacKay
Information Theory, Inference, and Learning Algorithms
(PDF available online)
Pattern Recognition and Machine Learning
Daphne Koller & Nir Friedman,
Probabilistic Graphical Models