Sharing knowledge through academic courses, professional workshops, and open educational resources. I believe in hands-on, practical learning that bridges theory and real-world application.
Learning happens best when students are actively engaged, challenged, and supported.
Every concept is taught with real-world applications. Students work on projects that matter, not just toy examples.
I create a classroom where questions are welcomed, mistakes are learning opportunities, and diverse perspectives are valued.
I explain not just what we're learning, but why and how it connects to students' goals and the broader field.
Regular, actionable feedback helps students improve continuously rather than just at the end of a project.
Courses I teach and have taught at the graduate and undergraduate levels
Comprehensive introduction to machine learning covering supervised, unsupervised, and reinforcement learning. Emphasis on mathematical foundations and practical implementation.
Introduction to core machine learning concepts for undergraduates. Focus on intuition, implementation in Python, and real-world applications.
Advanced course on optimization methods used in machine learning. Covers convex and non-convex optimization, stochastic methods, and modern deep learning optimizers.
Hands-on workshops for teams and organizations looking to build ML capabilities
A practical workshop for product managers and engineers on integrating ML into products. Covers when to use ML, setting realistic expectations, and collaboration best practices.
Audience: Product managers, engineers, executives
Hands-on introduction to deep learning with PyTorch. Participants build and train neural networks from scratch, gaining intuition for how deep learning works.
Audience: Data scientists, ML engineers, researchers
Workshop on building fair, transparent, and accountable AI systems. Covers bias detection, explainability tools, and governance frameworks.
Audience: ML practitioners, policy makers, leaders
Free learning materials, tutorials, and resources from my courses
Complete slide deck from my Machine Learning course, covering supervised and unsupervised learning.
Jupyter notebooks with hands-on exercises for learning PyTorch from basics to advanced topics.
Video recordings from my optimization course, available on YouTube.
Step-by-step tutorial on setting up ML pipelines with modern MLOps tools.
Feedback from students and workshop participants
“One of the best courses I've taken at Georgia Tech. Professor Vu explains complex concepts clearly and the projects are challenging but rewarding.”
Anonymous Student
CS 7641 - Machine Learning
I offer custom workshops for organizations, conferences, and academic institutions. Let's discuss how I can help your team level up their ML skills.