Machine Learning & AI
Machine Learning & AI
Learn about the latest in artificial intelligence.
Artificial intelligence (AI) mimics the human capacity for problem-solving. Machine learning (ML) is the branch of AI that trains on and learns from existing data to deliver relevant results. UCLA Extension offers expert-led courses exploring both.
Enhance your understanding or advance your career with our offerings designed for enthusiasts and professionals alike. Whether you're looking for a deep-dive into the technical aspects of ML or to explore the ethical and policy implications of AI, we have the perfect courses for you.
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Machine Learning & AI
Courses
COM SCI 910.2
Building Retrieval Augmented Generation (RAG)
This course teaches students to design and deploy Retrieval-Augmented Generation systems, combining LLMs with external data for accurate, scalable AI applications using modern tools, evaluation frameworks, and cloud platforms.
COM SCI X 450.45
Computer Vision: AI-Powered Image Understanding
This course provides a comprehensive exploration of computer vision and deep learning, equipping students with Python proficiency, image processing skills, and advanced neural network techniques to tackle real-world applications in healthcare, security, robotics, and automation through AI-driven solutions.
COM SCI 910.4
Context Engineering and AI Orchestration
Students learn advanced context engineering and AI orchestration through hands‑on projects, mastering prompting, context management, human‑in‑the‑loop design, ContextOps, and multi‑agent coordination to build secure, scalable, production‑ready AI systems.
COM SCI X 450.42
Deep Learning
Gain a robust understanding of deep learning through both theory and hands-on implementation, spanning domains such as computer vision, natural language processing (NLP) and graph data analysis. Explore neural network architectures, optimization techniques, and advanced models (CNNs, RNNs, GANs, GNNs).
COM SCI 910.1
Foundations of Generative AI
This course introduces generative AI through theory and hands-on practice, covering model evolution, practical techniques, and ethical frameworks to build, refine, and evaluate systems in real-world contexts.