Courses

ITS 36500 — Machine Learning Foundations

This course provides a basic introduction to the machine learning pipeline and related concepts. Topics covered include: Machine learning uses and applications; data set requirements; data pre-processing; data annotation, and validation; data representation formats; features and feature representation and extraction; the vector space model; traditional machine learning algorithms; machine learning algorithms and programming; ML evaluation methods; introduction to deep learning algorithms; big data; reinforcement learning; Unsupervised learning; statistical significance analysis; and other special topics.

Time & Place

Tue and Thu 6-8 pm

Textbook (Not Required)

1. Python Machine Learning, by Sebastian Raschka and Vahid Mirjalili

Instructor

Ricardo A. Calix, Ph.D.
Purdue University Northwest
rcalix@pnw.edu

Office Hours

241 Anderson

Office Hours

Thursday 2-4 pm (or by appointment)

Project

1. Recommender Systems

Videos

1. YouTube

Code

1. GitHub

Useful

• SEED Ubuntu pre-built Virtual Machine

Brightspace (Submit homework on Brightspace)

Brightspace

Datasets

• Fisher's iris
• MNIST train csv
• MNIST test csv

Lab Environment

Environment:

• Link

AWS

• You will need the .pem file provided with your subscription to AWS

WL Scholar

• Scholar

Course Materials

Labs

1. More materials on Brightspace

Recommendations on Sources and AI products

1. Recommendations

Background

Tools

We will use the following software:

1. Python
2. Anaconda
3. WEKA

Calendar Spring 2024 (subject to change)