Introduction to Learning
Key Takeaways
Introduction to supervised, unsupervised, and reinforcement learning using algorithms like decision trees and Q-learning
Original Description
This course introduces the foundational concepts of learning, focusing on supervised, unsupervised, and reinforcement learning. Students will learn how machines can learn from data to make predictions, find patterns, and make decisions over time. Topics include key algorithms such as decision trees, linear classifiers, clustering, and Q-learning. Students will develop a practical understanding of how learning systems work and how to apply them to real-world problems.
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