Special Topics in IE 2025

Today, we're setting up the foundation for data science and statistical analysis by installing R and RStudio. Whether you're a complete beginner or switching from another platform, this guide will get you up and running in no time. What is R and RStudio? R  is a powerful programming language specifically designed for statistical computing, data analysis, and graphics. It's free, open-source, and has an incredible ecosystem of packages for everything from basic statistics to advanced machine learning. RStudio  is an integrated development environment (IDE) that makes working with R much easier. Think of it as a user-friendly interface that sits on top of R, providing features like syntax highlighting, project management, and integrated plotting. Why Install Both? While you can use R by itself, RStudio provides: A clean, organized workspace Built-in help and documentation Easy package management Integrated plotting and visualization Project organization tools Git integration ...

Dr. Jomana Bashatah  Breaking Down Barriers, Building Understanding Machine learning is everywhere - from the apps on your phone to the systems that run modern businesses. Yet for many students, it feels like an intimidating black box reserved for math wizards and coding experts. This semester, we're changing that narrative. What This Course Is Really About ISE 423 isn't just another technical elective. It's your gateway to understanding one of the most transformative technologies of our time. But here's what makes this course different: we focus on practical understanding over theoretical complexity . You won't need advanced mathematics. You won't need programming experience. What you will need is curiosity and willingness to learn. Why I'm Blogging Our Journey Every semester, I watch students transform from "I can't/don't know how to do this" to "Wow I can't believe I just did that".  That transformation is worth do...

  ISE 423 Lecture 1 Recap What You DON'T Need (The Good News) Matrix algebra? Nope. Calculus? Not required. Programming experience? We'll learn R from scratch. The Buzzword Breakdown AI  = Making machines act smart (the big category) Machine Learning  = Teaching machines to learn from data (subset of AI) Deep Learning  = Neural networks (subset of ML) Big Data  = Really large datasets (not a type of learning!) Three Things ML Does Regression : Predict numbers (house prices, temperatures) Classification : Predict categories (spam/not spam, yes/no) Clustering : Find hidden patterns (customer groups you didn't know existed) The Process (It's Logical) Start with a model (but missing the key numbers) Train it using data (find those numbers) Get a fitted model (now it can predict) Test it on new data (make sure it actually works) Why This Matters You're already using ML everywhere - Netflix recommendations, Google searches, fraud detection. Now you'll understand how i...