(Image by Author: Conny Schneider. Unsplash.com)
"Hands-on Machine Learning: A Journey Through Data Science” covers essential concepts in classical machine learning, offered with beginner-friendly, hands-on programming demonstration in Python. We focus on one key algorithm, statistical concept, or tool each week and offer a real-world, hands-on data science application.
Dive into the tools and concepts you need to choose, design, and deploy ML models. Brush up on the fundamentals of big data analysis, and access our fantastic resources!
RESOURCES AND NOTES:
- Register to join in person or via Zoom.
- When: Tuesdays @ 1 PM
- Where: Weaver Science and Engineering Library, Room 212.
- Zoom: https://arizona.zoom.us/j/89667081542
There will be no workshop during Spring Break week: March 11.
Content schedule and content are subject to change.
| Date | Topic | Overview | Materials | Coding Examples | YouTube |
|---|---|---|---|---|---|
| 01/28 | Intro to Scikit-Learn | Scikit-Learn is a library in Python, which is a programming language. A library is like a toolbox that has many useful tools (or functions) that help you do specific tasks. In this case, Scikit-Learn helps you build and use machine learning models easily. | Notes | (no code) | video |
| 02/04 | Supervised Learning: Regression | Regression is a specific method used in supervised learning. It helps us predict a number. For example, if we want to predict how much a house will sell for based on its size, location, and number of bedrooms, we use regression. It finds a relationship between the input features and the output number. | Notes | Notebook | video |
| 02/11 | Supervised Learning: Classification | Classification is a specific task in supervised learning. It’s when we want the computer to sort things into different categories. For example, if we have pictures of animals, classification helps the computer decide if a picture is of a cat, dog, or bird. | Notes | Notebook | video |
| 02/18 | Unsupervised Learning: Dimensionality Reduction | Dimensionality Reduction is a method to simplify data by reducing the number of features or dimensions. Think of it like taking a big, complicated puzzle and making it smaller and easier to handle. For example, if you have a dataset with 100 different measurements for each item, dimensionality reduction helps to focus on the most important ones, maybe just 2 or 3. | Notes | Notebook | video |
| 02/25 | Unsupervised Learning: Clustering | Clustering is like grouping similar things together. Imagine you have a box of different colored candies. If you want to sort them, you might put all the red candies in one group, all the blue ones in another, and so on. In clustering, the computer does something similar with data, finding patterns and grouping similar items without any help. | Notes | Notebook | video |
| 03/04 | Ensemble Learning: Bagging | Bagging stands for "Bootstrap Aggregating." It is a technique that helps improve the accuracy of machine learning models. Instead of using just one model, bagging combines the predictions from many models to get a final answer. This is like asking several friends for their opinions before making a decision, rather than relying on just one person's view. | Notes | Notebook | video |
| 03/11 | Spring break | ||||
| 03/18 | Ensemble Learning: Boosting | Boosting is a way to improve the performance of a model by combining several weaker models to create a stronger one. Think of it like a team of players in a sport. Individually, they may not be the best, but together they can win games. | Notes | Notebook | video |
| 03/25 | Reinforcement Learning | Reinforcement Learning is a type of machine learning where an agent learns by interacting with its environment, taking actions and receiving feedback in the form of rewards, with the goal of maximizing those rewards over time, | Notes | Notebook | video |
| Date | Topic | Instructor | Materials | Coding Examples | YouTube |
|---|---|---|---|---|---|
| 03/21 | Perceptrons | Brenda Huppenthal | Notes | Notebook | video |
| 03/28 | Convolutional Neural Networks (CNN) | Brenda Huppenthal | Notes | Notebook | video |
| 04/04 | Recurrent Neural Networks (RNN) | Megh Krishnaswamy | Notes | Notebook | video |
| 04/11 | Generative Adversarial Networks (GAN) | Carlos Lizárraga | Notes | Notebook | video |
| 04/18 | Autoencoders | Brenda Huppenthal | Notes | Notebook | video |
| 04/25 | Large Language Models | Carlos Lizárraga | Notes | (No code) | video |
- UArizona DataLab Machine Learning Wiki
- Overview of Machine Learning Algorithms
- Digital Library: Collection of Machine Learning Books
Updated: 02/03/2025 (C. Lizárraga)
UArizona Data Lab, Data Science Institute, University of Arizona.
