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Introduction to Artificial Intelligence: every lesson, free with a free account

9 chapters · 69 video lessons with full transcriptsComputer Science 373 · 3 credits · recommended for credit by NCCRS

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Chapter 1Foundations of AI and Core Concepts

  1. 1.1 · 16 minWhat Is Artificial Intelligence? Origins, Ideas, and Growth
  2. 1.2 · 14 minCategorizing AI: Types and Distinctions
  3. 1.3 · 16 minSmart Agents and Automated Discovery Tools
  4. 1.4 · 15 minHow Algorithms Power Computer Programs
  5. 1.5 · 17 minTechniques for Computing Logarithmic Values
  6. 1.6 · 15 minCore Ideas in Probability and Chance
  7. 1.7 · 15 minMeasuring Data Quality: Precision, Accuracy, and Pitfalls
  8. 1.8 · 13 minMachine Learning vs. Artificial Intelligence: Key Differences

Chapter 2Intelligence, Cognition, and Agent Design

  1. 2.1 · 13 minLearnable Intelligence: Insights from Perkins' Framework
  2. 2.2 · 16 minEvaluating How We Think: Cognitive Function Analysis
  3. 2.3 · 14 minExpert Systems and AI Approaches to Complex Challenges
  4. 2.4 · 14 minBuilding Blocks of Intelligent Agents
  5. 2.5 · 11 minReactive Agents: Condition-Action Responses
  6. 2.6 · 10 minHow Model-Based Agents Track Their World
  7. 2.7 · 15 minAgents That Plan Toward Objectives
  8. 2.8 · 14 minMaximizing Outcomes: Utility-Driven Agent Behavior
  9. 2.9 · 12 minAgents That Improve Through Experience

Chapter 3Programming Foundations and Security

  1. 3.1 · 11 minWriting Pseudocode and Designing Step-by-Step Solutions
  2. 3.2 · 13 minProgramming Foundations: Writing, Testing, and Fixing Code
  3. 3.3 · 14 minHow Interpreters and Compilers Process Code
  4. 3.4 · 14 minRecognizing Threats in Computer Security
  5. 3.5 · 11 minReal-World Uses of Machine Learning

Chapter 4Search, Heuristics, and Game Strategy

  1. 4.1 · 15 minHandling Uncertainty and Measurement in Scientific Work
  2. 4.2 · 13 minApplying Bayes' Rule to AI Decisions
  3. 4.3 · 13 minBlind and Competitive Search Strategies
  4. 4.4 · 13 minHeuristic Approaches: Guided Problem Solving
  5. 4.5 · 12 minStrategic Reasoning: Game Theory in Intelligent Systems
  6. 4.6 · 13 minClimbing Toward Solutions: Local Search Methods
  7. 4.7 · 13 minPruning Game Trees with Alpha-Beta Techniques
  8. 4.8 · 13 minWorking with Constraint Satisfaction Problems
  9. 4.9 · 13 minSequential Decision Making with Markov Processes

Chapter 5Neural Networks and Learning Architectures

  1. 5.1 · 16 minProbabilistic Graphical Models for Learning
  2. 5.2 · 16 minHow Neural Networks Drive Machine Learning
  3. 5.3 · 11 minSplitting Data with Decision Tree Models
  4. 5.4 · 14 minForward-Flowing Neural Network Structures
  5. 5.5 · 12 minTraining Networks: Backpropagation and Gradient Methods
  6. 5.6 · 11 minImage-Focused Learning with Convolutional Networks
  7. 5.7 · 16 minSequence-Aware Learning with Recurrent Networks

Chapter 6Logic, Reasoning, and Knowledge Systems

  1. 6.1 · 13 minAnalytical Thinking and the Role of Mathematical Logic
  2. 6.2 · 15 minLogic and Computation in Intelligent Systems
  3. 6.3 · 13 minBuilding Truth Tables with Propositional Logic
  4. 6.4 · 10 minExpressing Knowledge Through First-Order Logic
  5. 6.5 · 14 minAlgorithms for Propositional Reasoning
  6. 6.6 · 16 minResolution-Based Inference in Logical Systems
  7. 6.7 · 13 minStructuring Domain Knowledge for AI Systems
  8. 6.8 · 13 minForward Chaining: Deriving Conclusions from Rules
  9. 6.9 · 11 minBackward Chaining: Reasoning from Goals to Evidence
  10. 6.10 · 16 minProlog: A Logic-Based Programming Language for AI

Chapter 7Machine Learning, NLP, and Perception

  1. 7.1 · 15 minLearning from Labeled Data: Supervised Techniques
  2. 7.2 · 16 minDiscovering Patterns Without Labels: Unsupervised Techniques
  3. 7.3 · 16 minCombining Natural Language Processing with Deep Learning
  4. 7.4 · 11 minInformation Gain and Entropy in Learning Models
  5. 7.5 · 11 minMaking Choices with Decision Networks
  6. 7.6 · 12 minClassifying Data with Support Vector Machines
  7. 7.7 · 13 minReasoning Under Uncertainty: Probabilistic Foundations
  8. 7.8 · 15 minModeling Change Over Time: Dynamic Probabilistic Systems

Chapter 8Perception, Robotics, and Hands-On AI

  1. 8.1 · 15 minHow Machines Understand Spoken Language
  2. 8.2 · 17 minTeaching Machines to Recognize and Sort Images
  3. 8.3 · 17 minLisp: The Language That Shaped Early AI
  4. 8.4 · 13 minMapping and Localization (SLAM) in Robotics
  5. 8.5 · 14 minDesigning and Programming Advanced Robotic Platforms

Chapter 9AI Applications, Ethics, and the Future

  1. 9.1 · 15 minNavigating the Ethical Landscape of AI
  2. 9.2 · 15 minAI in Everyday Life: Current Applications
  3. 9.3 · 16 minTransforming Healthcare Through Intelligent Systems
  4. 9.4 · 15 minAI Across Industries: Manufacturing, Logistics, and Beyond
  5. 9.5 · 12 minEmerging Directions in AI and Robotics
  6. 9.6 · 13 minHow AI Reshapes Economies and Markets
  7. 9.7 · 12 minThe Evolving Workplace: AI's Influence on Jobs and Skills
  8. 9.8 · 15 minAI's Broader Impact on Society, Policy, and Global Governance

FAQ

Questions about Introduction to Artificial Intelligence

Is this Introduction to Artificial Intelligence lesson free?
Yes, with a free UPI Study account. Every one of the 69 video lessons in Introduction to Artificial Intelligence has its own page with the full transcript, free to read; a free account unlocks the videos. The practice quizzes, graded work and college credit come with the course.
How many college credits is Introduction to Artificial Intelligence?
Introduction to Artificial Intelligence (Computer Science 373) carries a NCCRS credit recommendation for 3 semester credits. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.
How much does Introduction to Artificial Intelligence cost and what is included?
$250, paid once, with lifetime access: all 69 video lessons, a practice quiz after each, the graded work and final exam, and an official transcript when you finish. Students who want several courses choose UPI Study PRO instead.
Which universities accept UPI Study credits?
UPI Study has signed partner agreements with universities such as Thomas Edison State University, University of the People and Charter Oak State College, and works with universities such as Penn State, SNHU and SUNY. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities. Use "Find my college" on this page to check yours.
Can I download the Introduction to Artificial Intelligence syllabus?
Yes, the full syllabus is a free PDF download; it asks you to sign in with a free UPI Study account first.
How long does Introduction to Artificial Intelligence take?
It is self-paced with no deadlines. The 69 lessons total about 16 hours of video; most students add reading and practice time and finish in a few weeks to a few months, on their own schedule.
Do I need a prerequisite first?
Chapter 1 starts from what AI is and reviews the algorithms, logarithms and probability the course uses, so no AI background is needed; some programming experience helps. UPI Study also offers Data Structures and Algorithms and Programming in Python as separate courses.
How are the exams done?
Quizzes and assignments are online in the course. The final exam is taken online with proctoring; a person on the UPI Study team checks assignments, usually within two days.
How do my credits reach my college?
When the course shows 100% complete, request the official transcript from your dashboard. It is sent to the college you name, and you can send it to more than one. Check your college first with "Find my college" on this page, or ask Athena.
Is there a way to get every course, not just this one?
Yes. UPI Study PRO gives access to every current course, monthly, yearly, or once for Lifetime access. See the pricing page for the plans.