Special Topics in IE 2025

 The Saturday Afternoon Problem Before we dive into algorithms, let's think about how you already do machine learning without knowing it. You want to find someone to spend Saturday afternoon with. You're looking for your "nearest neighbor" based on: Gender (0 or 1) Age (in years) Outdoor sports interest (0-10 scale) Three candidates appear. Who's most similar to you (male, 50 years old, sports score 7)? Candidate 1: Male, 21 years old, score 5 → Average difference: 10.33 Candidate 2: Female, 51 years old, score 9 → Average difference: 1.33 Wait - the 21-year-old guy seems visually closer, but the math says the 51-year-old woman is more similar? This is the scaling problem we'll solve today. What Is k-Nearest Neighbors? It's the most intuitive machine learning algorithm: to classify something new, find what it's closest to and copy that label. If k=1: Find the single closest penguin and use its species If k=4: Find the 4 closest penguin...

ISE 423 Lecture 3 Recap What We're Actually Doing Today Stop thinking "programming" - start thinking "data manipulation." Today you learned to take messy data and turn it into exactly what you need, step by step. The Big Three Commands (Your New Best Friends) select() - Pick the columns you want (like choosing which Excel columns to keep)  filter() - Pick the rows you want (like Excel's AutoFilter, but way better)  mutate() - Create new columns based on existing ones (like Excel formulas, but cleaner) The Titanic Example (Real Data, Real Insights) We started with a dataset about Titanic passengers - 8 columns of information about who survived, their age, gender, ticket class, and fare paid. Step 1: Select what matters select(DataTitanic, Survived, Name, Gender, Age) Translation: "Just give me these 4 columns, ignore the rest." Step 2: Filter for who you want filter(DataTitanic, Gender == "female") Translation: "Only sh...

ISE 423 Lecture 2 Recap What You Actually Need (The Setup) Two things, that's it: R = The engine that runs your code RStudio = The friendly interface that makes R actually usable Think of it like a car: R is the engine, RStudio is everything else that makes driving pleasant. The RStudio Layout (Your New Workspace) RStudio gives you four panels that do different jobs: Top-left: Source Editor - Where you write and save your R scripts Bottom-left: Console - Where R actually runs your commands Top-right: Environment/History - See your variables and what you've done Bottom-right: Files/Plots/Packages - Manage everything else Essential Setup (Do This Once) Critical setting change: Go to Tools → Global Options and uncheck "Restore .RData into workspace at startup" Why? Because you want a fresh start every time, not mysterious leftover data from last week confusing you. Work with Projects: File → New Project. This keeps your analysis organized in its own...

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...