Course Curriculum

    1. Course Overview and Learning Outcomes

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    1. Ch 1.1 - What is Artificial Intelligence? - Definition & History

    2. Quiz 1.1

    3. Ch 1.2 - Types of Artificial Intelligence

    4. Quiz 1.2

    5. Ch 1.3 - Automated Online Search Tools & Intelligent Agents

    6. Quiz 1.3

    7. Ch 1.4 - What is an Algorithm in Programming? - Definition, Examples & Analysis

    8. Quiz 1.4

    9. Ch 1.5 - How to Evaluate Logarithms

    10. Quiz 1.5

    11. Ch 1.6 - Basic Probability Theory: Rules & Formulas

    12. Quiz 1.6

    13. Ch 1.7 - Accuracy, Precision & Types of Errors in Data Evaluation

    14. Quiz 1.7

    15. Ch 1.8 - Machine Learning vs. Artificial Intelligence

    16. Quiz 1.8

    17. Ch 1.9 - Perkins' Theory of Learnable Intelligence

    18. Quiz 1.9

    19. Ch 1.10 - Cognitive Function: Definition & Assessment

    20. Quiz 1.10

    21. Ch 1.11 - Using Artificial Intelligence (AI) and Expert Systems to Solve Complex Problems

    22. Quiz 1.11

    1. Ch 2.1 - Intelligent Agents: Definition, Types & Examples

    2. Quiz 2.1

    3. Ch 2.2 - Simple Reflex Agents: Definition, Uses & Examples

    4. Quiz 2.2

    5. Ch 2.3 - Model-based Agents: Definition, Interactions & Examples

    6. Quiz 2.3

    7. Ch 2.4 - Goal-based Agents: Definition & Examples

    8. Quiz 2.4

    9. Ch 2.5 - Utility-based Agents: Definition, Interactions & Decision Making

    10. Quiz 2.5

    11. Ch 2.6 - Learning Agents: Definition, Components & Examples

    12. Quiz 2.6

    1. Ch 3.1 - Writing Pseudocode: Algorithms & Examples

    2. Quiz 3.1

    3. Ch 3.2 - How to Write a Program: Coding, Testing & Debugging

    4. Quiz 3.2

    5. Ch 3.3 - What is a Computer Security Risk? - Definition & Types

    6. Ch 3.4 - Machine Learning: Techniques & Applications

    7. Quiz 3.4

    8. Ch 3.5 - Machine Code and High-level Languages: Using Interpreters and Compilers

    9. Quiz 3.5

    10. Ch 3.6 - Measurements & Uncertainty in Science

    11. Quiz 3.6

    12. Ch 3.7 - Using Bayes' Theorem in AI Decision-Making

    13. Quiz 3.7

    14. Ch 3.8 - Heuristic Methods in AI: Definition, Uses & Examples

    15. Quiz 3.8

    16. Ch 3.9 - Uninformed & Adversarial Searches in AI

    17. Quiz 3.9

    18. Ch 3.10 - Game Theory in Artificial Intelligence

    19. Quiz 3.10

    20. Ch 3.11 – Hill Climbing and Local Search Techniques

    21. Ch 3.12 – Alpha-Beta Pruning and Game Tree Optimization

    22. Ch 3.13 - Practical Application for Artificial Intelligence: AI Searches (Lab 1)

    1. Ch 4.1 - Constraint Satisfaction Problems: Definition & Examples

    2. Quiz 4.1

    3. Ch 4.2 - Bayes Networks in Machine Learning: Uses & Examples

    4. Quiz 4.2

    5. Ch 4.3 - Neural Networks in Machine Learning: Uses & Examples

    6. Quiz 4.3

    7. Ch 4.4 - Decision Tree Algorithm in Data Mining

    8. Quiz 4.4

    9. Ch 4.5 - Markov Decision Processes: Definition & Uses

    10. Quiz 4.5

    11. Ch 4.6 - Computational Logic: Methods & AI Applications

    12. Quiz 4.6

    13. Ch 4.7 - Simultaneous Localization and Mapping (SLAM): Definition & Importance

    14. Quiz 4.7

    15. Ch 4.8 - What is LISP in Artificial Intelligence?

    16. Quiz 4.8

    17. Ch 4.9 – Feedforward Neural Networks

    18. Ch 4.10 – Backpropagation and Gradient Descent

    19. Ch 4.11 – Convolutional Neural Networks (CNNs)

    20. Ch 4.12 – Recurrent Neural Networks (RNNs)

    1. Ch 5.1 - Critical Thinking and Logic in Mathematics

    2. Quiz 5.1

    3. Ch 5.2 - Propositions, Truth Values and Truth Tables

    4. Quiz 5.2

    5. Ch 5.3 - First-Order Logic in AI: Identification, Uses & Calculations

    6. Quiz 5.3

    7. Ch 5.4 - Propositional Logic Algorithms: Definition & Types

    8. Quiz 5.4

    9. Ch 5.5 - Knowledge Engineering in AI: Definition, Process & Examples

    10. Quiz 5.5

    11. Ch 5.6 - Forward Chaining in AI: Definition, Uses & Examples

    12. Quiz 5.6

    13. Ch 5.7 - Backward Chaining in AI: Definition, Uses & Efficiency

    14. Quiz 5.7

    15. Ch 5.8 - Prolog in AI: Definition & Uses

    16. Quiz 5.8

    17. Ch 5.9 - Practical Application for Artificial Intelligence: Backward Chaining (Lab 2)

    18. Ch 5.10 – Resolution in Logical Inference Systems

About this course

  • 136 lessons
  • 1750+ Transfer Colleges
  • ACE & NCCRS Approved
  • Globally Recognized

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    • 25% Attendance
    • 25% Quiz
    • 25% Assignments
    • 25% Final Exam

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