Intro to AI - Unit 8 Lecture - Elements of Machine Learning - Spring 2026

Описание: Recorded during a live class session on 3-2-26 for GTC.

This covers the materials for Unit 8 for the Intro to AI course at GTC.

These materials are based on Intel's AI for Workforce training materials in Artificial intelligence.

Topics for the Video:

This educational material provides a comprehensive look at the foundational elements of machine learning. It begins with a historical timeline of the field, tracing its evolution from early mathematical models to modern random forests and neural networks. The text distinguishes between supervised, unsupervised, and reinforcement learning, using a rat-in-a-maze analogy to clarify how agents interact with their environment to earn rewards. Furthermore, it introduces Q-Learning as a specialized, "greedy" method for optimizing decision-making through trial and error. The source concludes by comparing classic machine learning with deep learning, highlighting how the latter autonomously identifies relevant features for complex tasks like facial recognition and autonomous driving. Ethical concerns regarding AI accountability and regulation are also addressed to encourage responsible development.
This video is intended only for students in my course this semester, but others may also find it helpful.