Table of Contents
1. Introduction to AI
1.1 Intro to AI
1.2 Milestones in AI
1.3 Foundations of AI
1.4 Knowledge representation and search
1.5 Reasoning
1.6 Machine learning
1.7 Computer vision
1.8 Natural language processing
1.9 Robotics
1.10 Generative AI
2. Agents
2.1 Agents and environments
2.2 Rational agents and utility
2.3 Describing environments
2.4 Types of agents
3. Search
3.1 Defining a search problem
3.2 Search algorithms
3.3 Uninformed search algorithms
3.4 Informed search algorithms
3.5 Local search and optimization
4. Constraint Satisfaction
4.1 Defining constraint satisfaction problems (CSPs)
4.2 Inference in CSPs
4.3 Backtracking
4.4 Local search with constraints
4.5 Using structure to solve problems
5. Games
5.1 Game theory
5.2 Optimal decision making
5.3 Alpha-beta algorithm
6. Machine Learning
6.1 Supervised learning
6.2 Decision trees
6.3 Linear models
7. Reinforcement Learning
7.1 Reinforcement learning
7.2 Passive reinforcement
7.3 Active reinforcement
8. Deep Learning
8.1 Neural networks
8.2 Hidden layers
8.3 Training neural networks
8.4 Gradient descent
8.5 Normalization
8.6 Regularization
9. Computer Vision
9.1 Image processing
9.2 Convolutional neural networks
9.3 Image classification
9.4 Object detection
9.5 Image segmentation
10. Natural Language Processing
10.1 Language models
10.2 Text processing
10.3 Recurrent neural networks
10.4 Gated recurrent neural networks
10.5 Text classification
11. Large Language Models
11.1 Large language models
11.2 Transformers
11.3 Using LLMs
11.4 Fine-tuning LLMs
11.5 Prompt engineering
11.6 Retrieval augmented generation
12. Generative Neural Networks
12.1 Autoencoders
12.2 Generative adversarial networks
12.3 Diffusion models
12.4 Image generation
12.5 Language translation
13. Ethics
13.1 Risks and benefits of AI
13.2 Algorithmic bias
13.3 Hallucinations
13.4 Interpretability
13.5 Using AI responsibly
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Explore foundational concepts and modern AI
Artificial Intelligence introduces students to foundational concepts, algorithms, and modern applications of AI. Students will learn about classic topics in AI, like agents, search algorithms, constraint satisfaction problems, planning, and probability.
After building the foundation, Artificial Intelligence turns to modern AI with chapters on reinforcement learning, deep learning, computer vision, natural language processing, large language models, generative AI, and ethics. Interactive animations, learning questions, and Python demos support student learning and engagement. Challenge activities and zyLabs build on and assess students’ knowledge.
Dr. Aimee Schwab-McCoy explains the contents of the new Artificial Intelligence zyBook
zyBooks benefits students and instructors:
- Instructor benefits
- Customize your course by reorganizing existing content, or adding your own
- Continuous publication model updates your course with the latest content and technologies
- Gain insight into students’ progress, reading and participation with robust reporting
- Save time with auto-graded labs and challenge activities that seamlessly integrate with your LMS
- Build quizzes and exams
- Student benefits
- Learning questions and other content serve as an interactive form of reading
- Instant feedback on labs and homework
- Concepts come to life through extensive animations embedded into the interactive content
- Save chapters as PDFs to reference the material at any time
- More frequent, lower stakes assessments
Authors and contributors
Chinny Emeka
Content Author, Artificial Intelligence / PhD in Computer Science, University of Illinois at Urbana-Champaign
Pamela Fellers
Content Author, Data Science and Statistics / PhD in Statistics, University of Nebraska–Lincoln
Zoe Fox
Associate Content Author, Data Science and Statistics / BS in Mathematics and Statistics, University of British Columbia
Kate Nichols
Content Author, Computer Science / BA in Education, The University of Texas at Austin
Aimee Schwab-McCoy
Senior Manager, Content Development, Data Science, Mathematics and Statistics / PhD in Statistics, University of Nebraska–Lincoln
Dianna Spence
Content Author, Data Science and Statistics / PhD in Mathematics Education, Emory University
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