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

The Euclidean distance function for KNN and intro to Naive Bayes

video

Jan 26
 

Jan 27

Defining ML problems, matrices, vectors, numpy

video 

Jan 28
 
Jan 29 Jan 30
Jan 31

Feb 1

Numpy and the KNN algorithm in Python

video

Feb 2
 

Feb 3

Naive Bayes algorithm and Bayes Rule, numpy examples

video

Feb 4
 
Feb 5 Feb 6
Feb 7

Feb 8

Pipeline code and the Accuracy metric with numpy

video

The Gaussian function and Naive Bayes algorithm in numpy

video

Feb 9
 

Feb 10

Naive Bayes algorithm with numpy

video

Feb 11
 
Feb 12 Feb 13
Feb 14

Feb 15

Exam 1

Feb 16
 

Feb 17

Performance Metrics, Weka, feature ranking

video

Feb 18
 
Feb 19 Feb 20
Feb 21

Feb 22

Singular Value Decomposition and Recommender Systems

video

Feb 23
 

Feb 24

Singular Value Decomposition and Recommender Systems

video

Feb 25
 
Feb 26 Feb 27
Feb 28

Mar 1

Singular Value Decomposition and Dimensionality Reduction

video

Mar 2
 

Mar 3

Open lab: HWs, Term Project

How to use SVD and distance metrics to recommend movies

video

Mar 4
 
Mar 5 Mar 6
Mar 7

Mar 8

Reinforcement Learning, Taxi Cab

video

Mar 9
 

Mar 10

Reinforcement Learning, Taxi Cab

video

Mar 11
 
Mar 12 Mar 13
Mar 14 Mar 15 Mar 16 Mar 17 Mar 18 Mar 19 Mar 20
Mar 21

Mar 22


Neural networks intuition and a bit of history

video

Mar 23

Mar 24

The Perceptron

video

Mar 25
 
Mar 26 Mar 27
Mar 28

Mar 29

Linear regression

video

Mar 30
 

Mar 31

Logistic regression

video

Apr 1
 
Apr 2 Apr 3
Apr 4

Apr 5

Exam 2

Apr 6
 

Apr 7

Multi-Layer Perceptron

video

Apr 8
 
Apr 9 Apr 10
Apr 11

Apr 12

Deep Learning with Tensorflow and Keras

video

Apr 13
 

Apr 14

Work on Project
Apr 15
 
Apr 16 Apr 17
Apr 18

Apr 19

Deploying Tensorflow models to the web

demo

video

Apr 20
 

Apr 21

code

Apr 22
 
Apr 23 Apr 24
Apr 25 Apr 26
Project Presentations
 
Apr 27

Apr 28


Course Wrap-up

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