Courses

ITS 520 - Applied Machine Learning

This course will cover the fundamental concepts related to machine learning. Topics include:

• the basic machine learning pipeline
• data sets
• WEKA
• performance metrics
• optimization
• data scraping and data pre-processing
• features and the vector space model
• the bag of words approach for text 
• traditional machine learning algorithms
• sklearn
• Tensorflow 2.0
• numpy arrays and tensor operations
• features from images
• keras
• reinforcement learning
• unsupervised learning (clustering)
• unsupervised learning (compression with SVD)

Prerequisites: Programming skills up to data structures and a senior/graduate level course in statistics. Knowledge of Python and Linux.

Time Place

Monday 2-5 pm on Zoom. Zoom Meeting Room ID and code on Brightspace
 

Textbook

Python Machine Learning by Sebastian Raschka

Instructor

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

Office Hours

My office is at 241 Anderson

On-Line Office Hours

Thursday 2-4 pm (or by appointment) on Zoom. Zoom Meeting Room ID and code on Blackboard

About Purdue University Northwest

• PNW Campus Map
• PNW

Code

GitHub
 

Videos

• YouTube Channel

Brightspace (Submit homework on Brightspace)

Brightspace

Datasets

Lab Environment

Environment:

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

AWS

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

WL Scholar

• Scholar

Course Materials

Labs

1. More materials on Brightspace

Tools

We will use the following software:

1. Linux
2. Python
3. Anaconda

Calendar Fall 2020 (Subject to change)