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

Monday and Wednesday12-2 pm on Zoom. Zoom meeting ID and code on Brightspace

Textbook

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

2. Data Mining: Practical Machine Learning Tools and Techniques (latest edition). Authors: Witten, Frank

Instructor

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

Office Hours

241 Anderson

On-Line Office Hours

Thursday 2-4 pm (or by appointment) on Zoom. Zoom meeting ID and code on Brightspace

Project

1. project 1

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:

• Ubuntu 16.04 VM VM (download) for you to use. The VM password is: P@ssw0rd

AWS

• You will need the .pem file provided with your subscription to AWS
• Commands
• Instances
• Setting up an AWS instance

WL Scholar

• Scholar

Course Materials

Labs

1. More materials on Brightspace

Background

1. Introduction to Python Course

Tools

We will use the following software:

1. Linux
2. Python
3. Anaconda
4. WEKA

Calendar Spring 2021 (subject to change)