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Machine Learning (CS 446) Fall 2017

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Course Description

The goal of Machine Learning is to build computer systems that can adapt and learn from their experience. This course will study the theory and application of learning methods that have proved valuable and successful in practical applications. We review the theory of machine learning in order to get a good understanding of the basic issues in this area, and present the main paradigms and techniques needed to obtain successful performance in application areas such as natural language and text understanding, speech recognition, computer vision, data mining, adaptive computer systems and others. The main body of the course will review several supervised and (semi/un)supervised learning approaches. These include methods for learning linear representations, Bayesian / Probabilistic methods, decision-tree methods, kernel based methods and neural networks, as well as clustering and dimensionality reduction techniques. We will also discuss how to model machine learning problems and discuss some open problems.

Topics to be covered include:

Required text

Text: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press


Exam will be in class exam with 75 minutes. Please find information on our upcoming exams in the corresponding section of the class calendar.


Please see the class calendar for homework deadlines.


Course Staff

name and role contact ( office hours location
Sanmi Koyejo | Instructor sanmi Wednesday 2pm - 3pm or by appointment 3314 SC
Chase Duncan | Course Coordinator cddunca2 by appointment 3333 SC
Yeech Zhu | TA yzhu44 Thursday 6pm - 8pm Whiteboard outside of 3303 SC
Daniel Calzada | TA dcalzad2 Monday 7pm - 9pm Whiteboard outside of 3333 SC
Ping-Ko Chiu | TA pchiu5 Tuesday 4pm - 6pm Whiteboard outside of 3333 SC
Gaurush Hiranandani | TA gaurush2 Friday 11am - 1pm Whiteboard outside of 3333 SC


We will be using Python with the libraries numpy, scipy and matplotlib for assignments. No other languages are permitted. Python has a very gentle learning curve, so you should feel at home even if you've never done any work in Python.

Additional reading

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Python Help

Numpy Help

Linear Algebra