Machine Learning with Python
This workshop introduces fundamental machine learning concepts, algorithms, and practical modeling techniques. Students will learn to build, evaluate, and apply machine learning models.
Instructor: Wenbin Guo
Term: Fall, Winter, Spring
Location: Boyer Hall, Room 529
Time: Tuesdays to Thursdays, 1:00-4:00 PM
Overview
This workshop provides hands-on training in machine learning using Python, with a focus on understanding algorithms and applying models to real-world data. Students will:
- Understand machine learning paradigms, core concepts, and algorithm details
- Implement common machine learning methods in classification, regression, and unsupervised learning
- Evaluate and compare model performance using standard metrics and validation strategies
- Apply machine learning techniques to real-world problems involving high-dimensional data
For registration, visit the QCBio workshop page.
Prerequisites
- Prior programming experience in Python
- Basic knowledge of calculus, linear algebra, and statistics
Textbooks
- Primary: “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron
- Reference:
- “Machine Learning: A Probabilistic Perspective” by Kevin Murphy
- “An Introduction to Statistical Learning” by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
- “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Grading
- Assignments: 90%
- Participation: 10%
Schedule
| Date | Topic | Materials |
|---|---|---|
| Day 1 | Machine Learning Fundamentals An overview of core machine learning concepts, modeling workflows, and essential Python tools. | |
| Day 2 | Classification and Model Evaluation Classification algorithms, performance metrics, and model validation strategies. | |
| Day 3 | Regression and Unsupervised Learning Regression and regularization methods, dimensionality reduction, and clustering algorithms. |