Lectures

Lectures  

Lectures for Engineering Computation and Data Science. Requirements: Google Chrome; Laptop

Lecture 7 - Geospatial - Spatial/Temporal Information

Preparation Material - Videos/Exercises

Map - Hello World
Basic Timers
Repeating Timer

Mini Lecture

Geospatial

Active Learning

Globe Code ( github )
Real-Time Data
Geocoding Inspection

Lecture 8 - Non-Blocking, Async Computation

Preparation Material - Videos/Exercises

Node

Mini Lecture

Node - Requests, Promises, and Commands

Active Learning

Requests, Promises, and Commands

Lecture 10 - Regression and Random Walk

Preparation Material - Videos/Exercises

Random Walk
Random Walk (PDF)
Machine Learning 1 - Regression
Regression - statisitics approach
Regression Equation

Active Learning


Repo

Lecture 11 - K-Nearest Neighbors (kNN)

Mini Lecture

K-Nearest Neighbors

Active Learning

Exercise: Calculate Distance Between Points
Exercise: kNN

Lecture 12 - K-Means

Mini Lecture

K-Means

Active Learning

Exercise: Calculate Centroids
Exercise: kMeans
Animation

Lecture 13 - Naive Bayes Classification

Mini Lecture

Naive Bayes Classification

Active Learning

Document Classification
Solution

Vision Services API Example

Sample Code

Lecture 14 - Hardware and Communication

Mini Lecture

Hardware
Internet of Things

Active Learning

MQTT Basics
MQTT Channels

Exercises

LED Matrix
Chatroom
Sensor Data

Lecture 15 - Representational State Transfer

Mini Lecture

REST

Active Learning

REST, API, Data Store

Active Learning Solution

Solution