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Duration: 1 hour, 55 minutes

Duration: 52 minutes

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.

Duration: 2Ìýhours, 10 minutes

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.

Duration: 1Ìýhour, 9 minutes

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.

Duration: 1Ìýhour, 37 minutes

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.Ìý

Duration: 1Ìýhour

Final Exam Format: In-course, non-proctored exam

This module contains materials for the final exam. You must unlock the exam to earn a grade for the course.

• You may submit your exam only once.
• The exam contains only multiple choice questions.
• There are no programming questions in the exam.
• You are not allowed to use any notes or access other websites when you take your exam.