CS 441 AML - Applied Machine Learning

Coursera Link: https://www.coursera.org/learn/cs441aml

Instructor: Prof. Marco Morales Aguirre

Lead TA: Ehsan Saleh

Course Description

This course is intended for students who want to apply techniques of machine learning to various signal problems. The topics of this course are:

  • Classification
  • Regression
  • High Dimensional Data
  • Clustering
  • Graphical Models
  • Deep Neural Networks


The following references are also useful:

  • Kevin P. Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2021. You can find it at the author's website
  • Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006


You can find the full course syllabus here.

Tutorials and Technical Notes:

  1. Technical Note: Accessing the Course in Coursera
  2. Technical Note: Rough Estimates of the Implementation Sizes
  3. Technical Note: Receiving and Using the Failed Test Cases in Pre-computed Test Databases
  4. Technical Note: FAQ about Jupyter Notebooks
  5. Technical Note: Lab IDs, and Where to Find Them
  6. The Zen of Numpy; Things to Use More (or Less) Often in Numpy
  7. Technical Note: Question Posting Guidelines
  8. Announcement: All Assignments Are Released