Courses

ITS 520 - Applied Machine Learning

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

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

Code

GitHub
 

Videos

Brightspace (Submit homework on Brightspace)

Brightspace

Datasets

Lab Environment

Environment:

AWS

WL 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)

Mon Tue Wed Thu Fri

Aug 24

Intro to the course 

video

Aug 25

 

Aug 26

Machine Learning motivation

video

Aug 27

 
Aug 28

Aug 31

Basics of machine learning

video

Sep 1
 

Sep 2

Weka

video

Sep 3
 
Sep 4
Sep 7 Sep 8
 
Sep 9 Sep 10
 
Sep 11

Sep 14

ML performance metrics

video

Sep 15
 

Sep 16

WL GPU Scholar

Anaconda

video

Sep 17
 
Sep 18

Sep 21

data sets and features

video

Sep 22
 

Sep 23

features and datasets

video
 

Sep 24
 
Sep 25

 

Sep 28

Optimization and gradient descent

video

Sep 29
 

Sep 30

sklearn, CountVectorizer, bag of words approach

video

Oct 1
 
Oct 2

Oct 5

Scholar issues

video
 

Oct 6
 

Oct 7
 

sklearn

video

Oct 8
 
Oct 9

 
Oct 12

 
Oct 13
 
Oct 14

 
Oct 15
 
Oct 16
 

Oct 19

Corpus, samples, features, and labels

video
 

Oct 20

 

Oct 21

Intro to Tensorflow 2.0

video

Oct 22

 
Oct 23

 

Oct 26

Parts of a machine learning algorithm in Tensorflow

video

Oct 27

 

Oct 28

Mid-Term Exam
 

Oct 29

 
Oct 30

 

Nov 2

Tensorflow 2.0 basics

video

Nov 3

 

Nov 4

Tensorflow 2.0 basics and intro to Keras

video

Nov 5

 
Nov 6
 

Nov 9

Tensorflow 2.0 Tensor operations and the dataset module

video

Nov 10
 

Nov 11

Tensorflow 2.0 Loading images and tfds

video

Nov 12
 
Nov  13

Nov 16

XOR problem in Tensorflow 2.0 and Keras

video

Nov 17
 

Nov 18

Tensorflow 2.0 and Keras
Nov 19
 
Nov 20
 

Nov 23

Re-inforcement Learning with Q-Learn (chapter)

video

Nov 24
 

Nov 25

Re-inforcement Learning with Q-Learn

video

Nov 26
 
Nov 27

Nov 30

Unsupervised Learning

Dec 1
 

Dec 2
 

Singular Value Decomposition (SVD) and Recommender Systems
Dec 3
 
Dec 4
 

Dec 7

Presentations

Dec 8
 

Dec 9
 

Presentations

Dec 10
 
Dec 11
 
Dec 14
Finals
Dec 15
Finals
Dec 16
Finals
Dec 17
Finals
Dec 18
Finals