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Introduction to
Artificial
Intelligence
Understand how AI actually works, machine learning, problem-solving, and intelligent systems, before the field shapes you instead.
What You'll Learn
A full semester of introduction to artificial intelligence — completed at your own pace, with no deadlines or fixed class times.
What You'll Be Able to Do
- Interpret key Artificial Intelligence (AI) concepts and history, describe the distinctions between AI types and models, and classify various AI systems in real-world contexts
- Analyze algorithms and intelligent agents, compute and interpret logarithmic functions and probability theories in AI, and evaluate data accuracy and precision
- Compare and contrast machine learning and AI, apply theoretical knowledge to develop AI-driven solutions, and implement AI programming languages like LISP and Prolog
- Design intelligent agents, evaluate decision-making models, and synthesize knowledge of Bayesian networks, neural networks, and decision trees to create AI solutions
- Analyze AI's ethical implications, manage AI projects using advanced functionalities, and predict future AI technology directions and their societal impacts.
Major Course Topics
Course chapters and lessons
Introduction to Artificial Intelligence has 9 chapters across 44 instructional hours, as listed in the course syllabus. Expand any chapter to see the lessons inside it.
- Chapters
- 9
- Lessons
- 72
- Instructional hours
- 44 hrs
- Semester hours
- 3
Chapter 1
Foundations of AI and Core Concepts
- 1.1 What Is Artificial Intelligence? Origins, Ideas, and Growth
- 1.2 Categorizing AI: Types and Distinctions
- 1.3 Smart Agents and Automated Discovery Tools
- 1.4 How Algorithms Power Computer Programs
- 1.5 Techniques for Computing Logarithmic Values
- 1.6 Core Ideas in Probability and Chance
- 1.7 Measuring Data Quality: Precision, Accuracy, and Common Pitfalls
- 1.8 Machine Learning vs. Artificial Intelligence: Key Differences
Chapter 2
Intelligence, Cognition, and Agent Design
- 2.1 Learnable Intelligence: Insights from Perkins' Framework
- 2.2 Evaluating How We Think: Cognitive Function Analysis
- 2.3 Expert Systems and AI Approaches to Complex Challenges
- 2.4 Building Blocks of Intelligent Agents
- 2.5 Reactive Agents: Condition-Action Responses
- 2.6 How Model-Based Agents Track Their World
- 2.7 Agents That Plan Toward Objectives
- 2.8 Maximizing Outcomes: Utility-Driven Agent Behavior
- 2.9 Agents That Improve Through Experience
Chapter 3
Programming Foundations and Security
- 3.1 Writing Pseudocode and Designing Step-by-Step Solutions
- 3.2 Programming Foundations: Writing, Testing, and Fixing Code
- 3.3 How Interpreters and Compilers Process Code
- 3.4 Recognizing Threats in Computer Security
- 3.5 Real-World Uses of Machine Learning
Chapter 4
Search, Heuristics, and Game Strategy
- 4.1 Handling Uncertainty and Measurement in Scientific Work
- 4.2 Applying Bayes' Rule to AI Decisions
- 4.3 Blind Search and Competitive Search Strategies
- 4.4 Heuristic Approaches: Guided Problem Solving
- 4.5 Strategic Reasoning: Game Theory in Intelligent Systems
- 4.6 Climbing Toward Solutions: Local Search Methods
- 4.7 Pruning Game Trees with Alpha-Beta Techniques
- 4.8 Working with Constraint Satisfaction Problem
- 4.9 Sequential Decision Making with Markov Processes
- 4.10 Hands-On Lab 1: Implementing AI Search Algorithms
Chapter 5
Neural Networks and Learning Architectures
- 5.1 Probabilistic Graphical Models for Learning
- 5.2 How Neural Networks Drive Machine Learning
- 5.3 Splitting Data with Decision Tree Models
- 5.4 Forward-Flowing Neural Network Structures
- 5.5 Training Networks: Backpropagation and Gradient Methods
- 5.6 Image-Focused Learning with Convolutional Networks
- 5.7 Sequence-Aware Learning with Recurrent Networks
Chapter 6
Logic, Reasoning, and Knowledge Systems
- 6.1 Analytical Thinking and the Role of Mathematical Logic
- 6.2 Logic and Computation in Intelligent Systems
- 6.3 Building Truth Tables with Propositional Logic
- 6.4 Expressing Knowledge Through First-Order Logic
- 6.5 Algorithms for Propositional Reasoning
- 6.6 Resolution-Based Inference in Logical Systems
- 6.7 Structuring Domain Knowledge for AI Systems
- 6.8 Forward Chaining: Deriving Conclusions from Rules
- 6.9 Backward Chaining: Reasoning from Goals to Evidence
- 6.10 Prolog: A Logic-Based Programming Language for AI
- 6.11 Hands-On Lab 2: Building a Backward Chaining System
Chapter 7
Machine Learning, NLP, and Perception
- 7.1 Learning from Labeled Data: Supervised Techniques
- 7.2 Discovering Patterns Without Labels: Unsupervised Techniques
- 7.3 Combining Natural Language Processing with Deep Learning
- 7.4 Information Gain and Entropy in Learning Models
- 7.5 Making Choices with Decision Networks
- 7.6 Classifying Data with Support Vector Machines
- 7.7 Reasoning Under Uncertainty: Probabilistic Foundations
- 7.8 Modeling Change Over Time: Dynamic Probabilistic Systems
Chapter 8
Perception, Robotics, and Hands-On AI
- 8.1 How Machines Understand Spoken Language
- 8.2 Teaching Machines to Recognize and Sort Images
- 8.3 LISP: The Language That Shaped Early AI
- 8.4 Mapping and Localization (SLAM) in Robotics
- 8.5 Designing and Programming Advanced Robotic Platforms
- 8.6 Hands-On Lab 3: Building a Conversational AI Agent
Chapter 9
AI Applications, Ethics, and the Future
- 9.1 Navigating the Ethical Landscape of AI
- 9.2 AI in Everyday Life: Current Applications
- 9.3 Transforming Healthcare Through Intelligent Systems
- 9.4 AI Across Industries: Manufacturing, Logistics, and Beyond
- 9.5 Emerging Directions in AI and Robotics
- 9.6 How AI Reshapes Economies and Markets
- 9.7 The Evolving Workplace: AI's Influence on Jobs and Skills
- 9.8 AI's Broader Impact on Society, Policy, and Global Governance
These chapters come straight from the official course syllabus, which also sets out the graded assignment and the proctored final exam.
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Structured lessons covering every topic — designed as a standard Introduction to Artificial Intelligence college-level course.
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