AI 101 - Course & Competition - Grades 7-12 - Sun@11:30am EST-Recording Workshop 1

Video Transcription

Video Summary

This workshop launches an AI 101 course, introducing artificial intelligence fundamentals and the instructional team from MIT, Stanford, and Microsoft. The course syllabus covers foundational AI topics such as decision trees, machine learning, neural networks, GANs, NLP, and reinforcement learning. Emphasis is placed on respectful classroom engagement, timely homework submission via Google Classroom, and active participation through questions.<br /><br />The session defines AI as machines exhibiting intelligence—learning, reasoning, and problem-solving—via rule-based or learning-based systems. Key AI fields include machine learning, natural language processing, and computer vision, with applications spanning healthcare, finance, retail, automotive, and marketing.<br /><br />The main focus is decision trees, a supervised learning model used for classification and regression tasks. Decision trees are visualized as upside-down trees with root, internal (decision), and leaf (terminal) nodes connected by branches, making them highly interpretable. Examples like scholarship eligibility and loan approval illustrate their structure and decision-making flow.<br /><br />Advantages of decision trees include ease of understanding, handling mixed data types, and managing missing values, while drawbacks include sensitivity to data changes, risk of overfitting, and potential bias from imbalanced data.<br /><br />The session introduces Gini impurity, a metric for measuring data impurity in nodes, crucial for building trees. The ideal Gini impurity is zero, indicating uniform classification, while the maximum impurity for two classes is 0.5.<br /><br />Homework involves calculating Gini indices, understanding impurity ranges for multiple classes, and deciding on splits based on impurity values, preparing students to construct decision trees in-depth next week.

Keywords

Artificial Intelligence

AI 101 Course

Decision Trees

Machine Learning

Neural Networks

Gini Impurity

Natural Language Processing

Reinforcement Learning

AI Applications