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

Brightspace (Submit homework on Brightspace)

Brightspace

Datasets

Lab Environment

Environment:

AWS

WL 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)

Sun Mon Tue Wed Thu Fri Sat
Jan 10

Jan 11

Introduction

video

Jan 12
 

Jan 13
The ML pipeline (link)

Feature Engineering problems

video

Jan 14
 
Jan 15 Jan 16
Jan 17 Jan 18 Jan 19

Jan 20

The vector space model and KNN

video

Jan 21

Lab: 

Jan 22 Jan 23
Jan 24

Jan 25

Naive Bayes

Jan 26
 

Jan 27

Jan 28
 
Jan 29 Jan 30
Jan 31

Feb 1

Performance Metrics

Feb 2
 

Feb 3

Lab: Weka

Feb 4
 
Feb 5 Feb 6
Feb 7

Feb 8

Feature Pre-Processing

Feb 9
 

Feb 10

Chi-Square

Feb 11
 
Feb 12 Feb 13
Feb 14

Feb 15

Dimensionality Reduction

Feb 16
 

Feb 17

Singular Value Decomposition

Feb 18
 
Feb 19 Feb 20
Feb 21

Feb 22

Recommender Systems

Feb 23
 
Feb 24 Feb 25
 
Feb 26 Feb 27
Feb 28

Mar 1

Reinforcement Learning

Mar 2
 

Mar 3

Taxi Cab

Mar 4
 
Mar 5 Mar 6
Mar 7

Mar 8

Optimization

Mar 9
 
Mar 10 Mar 11
 
Mar 12 Mar 13
Mar 14 Mar 15 Mar 16 Mar 17 Mar 18 Mar 19 Mar 20
Mar 21 Mar 22
Backpropagation
Mar 23 Mar 24 Mar 25
 
Mar 26 Mar 27
Mar 28

Mar 29

Feedforward Algorithms

Mar 30
 

Mar 31

Neural Networks

Apr 1
 
Apr 2 Apr 3
Apr 4

Apr 5

Keras

Apr 6
 

Apr 7

DNN

Apr 8
 
Apr 9 Apr 10
Apr 11

Apr 12

Sequence Mining

Apr 13
 

Apr 14

Hidden Markov Models

Apr 15
 
Apr 16 Apr 17
Apr 18

Apr 19

K-Means

Apr 20
 

Apr 21

GMMs

Apr 22
 
Apr 23 Apr 24
Apr 25 Apr 26
Project Presentations
 
Apr 27 Apr 28
Project Presentations
 
Apr 29 Apr 30 May 1
May 2 May 3
Finals
May 4
Finals
May 5
Finals
May 6
Finals
May 7
Finals
May 8