How Do Computers Learn?

This module introduces students to one of the most fascinating and essential aspects of Artificial Intelligence: machine learning. Rather than relying on step-by-step instructions, machine learning allows computers to improve their performance by recognizing patterns in data and learning from experience — much like humans do.

Students will explore the basic concepts behind how machines "learn," including supervised learning (learning from labeled data), classification, prediction, and feedback loops. Through interactive exercises and real-world analogies, they will begin to understand how algorithms use large amounts of data to make decisions, improve accuracy, and adapt to new information over time.

Activities may include training a simple model using tools like Google’s Teachable Machine or creating their own rule-based vs. learning-based systems to see the difference in how computers handle tasks. By engaging directly with the learning process, students will gain insight into key terms such as data sets, features, labels, and models.

This module also introduces early discussions about the quality of data and its influence on how well a machine can learn — setting the stage for deeper exploration into topics like bias, fairness, and data ethics in future modules.

By the end of this module, students will not only understand how computers learn but will also begin to see the importance of good data, experimentation, and feedback in developing intelligent systems.