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Starts 24 June 2026 11:36

Ends 24 June 2026

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Introduction to Learning

Explore foundational AI learning concepts—supervised, unsupervised, and reinforcement learning—covering decision trees, neural networks, and Q-learning to build practical skills for real-world applications.
University of Colorado Boulder via Coursera

University of Colorado Boulder

38 Courses

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4 weeks, 1 hour a week

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Overview

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.

Syllabus

  • Foundations of Learning in AI
    • This module introduces the foundational ideas behind learning in artificial intelligence. Students begin by exploring what it means for an intelligent system to learn and how learning differs from simply following pre-programmed rules. The module then connects learning to the broader framework of intelligent agents, examining how agents improve performance through experience, feedback, and interaction with their environments. Finally, the module surveys the three major paradigms of machine learning—supervised learning, unsupervised learning, and reinforcement learning.
  • Foundations of Machine Learning - Prediction and Structure
    • This module introduces how AI systems learn from data and use that knowledge to make predictions, discover patterns, and improve performance. Students explore supervised learning with labeled examples, including the distinction between classification and regression problems, as well as unsupervised learning methods that uncover structure and relationships in unlabeled data. The module also examines latent and hidden variables, connecting these ideas to probabilistic models such as Bayes nets and Hidden Markov Models.
  • Generalization and Evaluation in Machine Learning
    • This module examines the central challenge of learning: building models that generalize effectively to new, unseen data. Students explore the concepts of overfitting, underfitting, and the bias-variance tradeoff, along with the processes involved in training and evaluating learning models. The module also introduces the roles of training, validation, and testing data sets in model development and examines the practical challenges that arise in AI learning systems, including data limitations, optimization difficulties, scalability, and changing environments.
  • Learning Models
    • This module introduces major families of AI and machine learning models, including linear models, decision trees, neural networks, and reinforcement learning. Students explore how each model family represents knowledge, learns from data or experience, and makes decisions or predictions. The module also connects these classical and modern learning approaches to contemporary AI systems such as large language models, recommendation systems, and robotics.

Taught by

Rhonda Hoenigman

Subjects

Computer Science

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