Applied Machine Learning

Fall 2018, INFO-4604, University of Colorado Boulder

Instructors:

Prof. Michael Paul
Arcadia Zhang (TA)

(Office hour: Tuesdays 11:30-12:30, TLC 266)
(Office hour: Wednesdays 11:30-12:30, TLC 215)

Time/Place:

Tuesday/Thursday 3:30pm–4:45pm, DUAN G125

Canvas:

https://canvas.colorado.edu/courses/22139

Textbook:

Raschka and Mirjalili (2017) Python Machine Learning, 2nd Edition.

Introduces algorithms and tools for building intelligent computational systems. Methods will be surveyed for classification, regression and clustering in the context of applications such as document filtering and image recognition. Students will learn the theoretical underpinnings of common algorithms (drawing from mathematical disciplines including statistics and optimization) as well as the skills to apply machine learning in practice.

machine learning

Prerequisites:

One of INFO 2201, INFO 2301, CSCI 2270, CSCI 3022, or equivalent.
Students must be able to program in Python, or be willing to quickly learn.

Textbooks

Required: Raschka and Mirjalili (2017) Python Machine Learning, 2nd Edition.

The 1st edition is also acceptable. The main difference between the two editions is that the newer edition covers deep learning and TensorFlow, which is not covered in this course.
If you have the 1st edition, note that errata are available online. The website shows you how to tell if you have a corrected version of the text.

Required for 5604: Shalev-Shwartz and Ben-David (2014) Understanding Machine Learning: From Theory to Algorithms.

This book is more advanced than the scope of this course, but occasionally readings from this book will be assigned, more often for the graduate version of this course.
A PDF version of this book is available for free online.

Recommended: Murphy (2012) Machine Learning: a Probabilistic Perspective.

This is a standard textbook for machine learning courses, with comprehensive coverage of the field. It is useful both as an introduction and as a reference book for students who want to do research in machine learning.

Recommended: Diez, Barr, Çetinkaya-Rundel (2015) OpenIntro Statistics, 3rd Edition.

This book introduces many of the mathematical concepts used in this course, and you may find it to be a helpful supplement. Chapters 2-3 on probability may be especially helpful.
A PDF version of this book is available for free online.

Code

Code from the textbook:

https://github.com/rasbt/python-machine-learning-book-2nd-edition

Python libraries used in this course:

Course Materials

Websites from past semesters:

Fall 2017