Course Curriculum

    1. Course Overview and Learning Outcomes

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    1. VIDEO:What are algorithms

    2. Ch 1.1 - Defining Artificial Intelligence: Concepts and Evolution

    3. Quiz 1.1

    4. Ch 1.2 - Classifying Artificial Intelligence

    5. Quiz 1.2

    6. Ch 1.3 - Intelligent Agents and Automated Search Tools

    7. Quiz 1.3

    8. Ch 1.4 - Understanding Algorithms in Programming

    9. Quiz 1.4

    10. Ch 1.5 - Methods of Evaluating Logarithms

    11. Quiz 1.5

    12. Ch 1.6 - Principles of Probability Theory

    13. Quiz 1.6

    14. Ch 1.7 - Data Evaluation: Accuracy, Precision, and Error Types

    15. Quiz 1.7

    16. Ch 1.8 - Comparing Machine Learning with Artificial Intelligence

    17. Quiz 1.8

    18. Ch 1.9 - Exploring Perkins' Theory of Learnable Intelligence

    19. Quiz 1.9

    20. Ch 1.10 - Assessing Cognitive Functions

    21. Quiz 1.10

    22. Ch 1.11 - AI and Expert Systems for Complex Problem Solving

    23. Quiz 1.11

    1. Ch 2.1 - Fundamentals of Intelligent Agents

    2. Quiz 2.1

    3. Ch 2.2 - Simple Reflex Agents

    4. Quiz 2.2

    5. Ch 2.3 - Dynamics of Model-Based Agents

    6. Quiz 2.3

    7. Ch 2.4 - Goal-Oriented Agents Explained

    8. Quiz 2.4

    9. Ch 2.5 - Decision Making in Utility-Based Agents

    10. Quiz 2.5

    11. Ch 2.6 - Learning Agents and Their Components

    12. Quiz 2.6

    1. VIDEO:Machine Learning, Deep Learning, and Neural Networks-The Basics of Artificial Intelligence Explained

    2. Ch 3.1 - Pseudocode and Algorithm Development

    3. Quiz 3.1

    4. Ch 3.2 - Programming Essentials: From Coding to Debugging

    5. Quiz 3.2

    6. Ch 3.3 - Understanding Computer Security Risks

    7. Ch 3.4 - Applications of Machine Learning

    8. Quiz 3.4

    9. Ch 3.5 - Interpreters and Compilers in Programming

    10. Quiz 3.5

    11. Ch 3.6 - Measurements & Uncertainty in Science

    12. Quiz 3.6

    13. Ch 3.7 - Bayesian Theorem in AI Decision-Making

    14. Quiz 3.7

    15. Ch 3.8 - Heuristic Methods in AI

    16. Quiz 3.8

    17. Ch 3.9 - Strategies for Uninformed and Adversarial Search

    18. Quiz 3.9

    19. Ch 3.10 - The Role of Game Theory in AI

    20. Quiz 3.10

    21. Ch 3.11 – Hill Climbing and Local Search Techniques

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

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

    1. VIDEO:Machine Learning, Deep Learning, and Neural Networks-The Basics of Artificial Intelligence Explained

    2. VIDEO:Convolutional Neural Nets (CNN) & Backpropagation = How Machines Learned to See

    3. VIDEO:What are Recurrent Neural Nets (RNN) How Siri, Alexa, and Google Understand You

    4. Ch 4.1 - Solving Constraint

    5. Quiz 4.1

    6. Ch 4.2 - Bayesian Networks for Machine Learning

    7. Quiz 4.2

    8. Ch 4.3 - Utilizing Neural Networks in Machine Learning

    9. Quiz 4.3

    10. Ch 4.4 - Decision Trees for Data Analysis

    11. Quiz 4.4

    12. Ch 4.5 - Markov Decision Processes

    13. Quiz 4.5

    14. Ch 4.6 - Computational Logic in AI

    15. Quiz 4.6

    16. Ch 4.7 - The Importance of SLAM in Robotics

    17. Quiz 4.7

    18. Ch 4.8 - LISP Programming: AI's Historical Language

    19. Quiz 4.8

    20. Ch 4.9 – Feedforward Neural Networks

    21. Ch 4.10 – Backpropagation and Gradient Descent

    22. Ch 4.11 – Convolutional Neural Networks (CNNs)

    23. Ch 4.12 – Recurrent Neural Networks (RNNs)

    1. Ch 5.1 - Critical Thinking and Mathematical Logic

    2. Quiz 5.1

    3. Ch 5.2 - Propositional Logic and Truth Tables

    4. Quiz 5.2

    5. Ch 5.3 - First-Order Logic in AI

    6. Quiz 5.3

    7. Ch 5.4 - Propositional Logic Algorithms

    8. Quiz 5.4

    9. Ch 5.5 - The Process of Knowledge Engineering in AI

    10. Quiz 5.5

    11. Ch 5.6 - Forward Chaining: A Rule-Based AI Technique

    12. Quiz 5.6

    13. Ch 5.7 - Backward Chaining: Efficiency and Applications

    14. Quiz 5.7

    15. Ch 5.8 - Prolog: Programming for AI

    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

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

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    General Questions

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    YES, there are exams—held online on our platform and are proctored. Your course grade distribution is:

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

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  • Will there be hand holding to help transfer courses?

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