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AI and Machine Learning: Concepts, Applications, and Ethical Consideration, 1st Edition
Overview
What if students could move beyond simply using AI tools to actually understanding, developing, and deploying them responsibly?
"Artificial Intelligence and Machine Learning" by Riggins and Osei is a complete instructional resource that guides learners from core concepts to the ethical design, development, and deployment of real AI systems. Designed to help students overcome common classroom challenges, including black-box thinking, ethical blind spots, technical barriers, and the gap between theory and practice, it emphasizes Explainable AI, embedded ethics, concept-first mathematics, and flexible pathways from no-code tools to Python.
With a project-build approach, its content engages students in creating a meaningful AI solution across the full development lifecycle. Supported by rich instructor resources, assessments, and case studies, this resource makes AI education accessible, rigorous, and relevant across disciplines.
- Connect every chapter to a meaningful, real-world outcome through a semester-long AI agent project.
- Help students identify bias, hallucinations, and flawed outputs with built-in XAI tools and critical auditing exercises.
- Support business-focused learners with no-code tools, as well as technical students with Python and machine learning.
- Move beyond the algorithm by teaching students to build, deploy, monitor, and optimize real AI systems from concept to production.
- Transform AI math from a barrier into a bridge with concept-first explanations that make complex ideas accessible and memorable.
- Harness AutoML and AI-assisted coding to boost productivity while building programming, debugging, and critical thinking skills.
- Make computer science concepts stick with interactive videos, visualizations, and practice tools in MindTap that drive greater understanding.
- Help students explain, analyze, and apply AI responsibly with Applied AI Skill Video Recording Assessments that build AI literacy and career-ready skills.
Ethical Impact of AI
Introduction to Machine Learning
Programming for Machine Learning Applications
Data for Machine Learning
Understanding Math Fundamentals for Machine Learning Applications
Computer Vision
Natural Language Processing
Reinforcement Learning
Deep Learning: A Subset of AI and a Key Technology Driving Modern AI Systems
Neural Networks: A Foundational Concept within AI and Central to Machine Learning
Future of AI & Machine Learning
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