CS 373: Introduction to Artificial Intelligence

The objective of this course is to teach students the foundations and applications of artificial intelligence. Students will learn about intelligent agents, constraint satisfaction, logical agents, first-order logic, learning, reasoning, and AI trend

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Course Curriculum

1. Introduction
1. What Is Artificial Intelligence?
2. Brief History
3. Current State & Future
4. Lesson Summary
5. Reference Videos
6. Quiz 1.1
1. Dabbling in Artificial Intelligence
2. What Is Artificial Intelligence?
3. Types of Artificial Intelligence
4. Lesson Summary
5. Quiz 1.2
6. Reference Videos
1. Avoiding Boredom
2. What is an Automated Online Search Tool?
3. What is an Intelligent Agent?
4. What Are the Differences Between the Two?
5. Lesson Summary
6. Quiz 1.3
7. Refence Videos
1. What Is a Programming Algorithm?
2. Programming Algorithm Example
3. Lesson Summary
4. Key Terms
5. Learning Outcomes
6. Quiz 1.4
1. Quick Review of Logarithmic Form
2. Problem #1
3. Problem #2
4. Problem #3
5. Problem #4
6. Log Base 10 and Natural Log
7. Change of Base Formula
8. Lesson Summary
9. Quiz 1.5
1. Probability Basics
2. Visualizing Probability
3. Probability Rules
4. Law of Total Probability
5. Bayes' Theorem
6. Lesson Summary
7. Quiz 1.6
8. Reference Videos
1. Why Evaluate Data?
2. Accuracy vs Precision
3. Types of Error
4. Lesson Summary
5. Learning Outcomes
6. Quiz 1.7
7. Reference Videos
1. Our Fascination with Inanimate Intellect
2. What is Machine Learning?
3. Types of Machine Learning
4. What is Artificial Intelligence?
5. Machine Learning vs Artificial Intelligence
6. Lesson Summary
7. Quiz 1.8
8. Reference Videos
1. David Perkins and Intelligence
2. Three Types of Intelligence
3. Learnable Intelligence
4. Lesson Summary
5. Learning Outcomes
6. Quiz 1.9
1. What Is a Cognitive Function?
2. Memory, Decision Making, and Problem Solving
3. Attention, Language, and Perception
4. Cognitive Function Assessment
5. Cognitive Function and Aging
6. Lesson Summary
7. Quiz 1.10
1. What Is Intelligence?
2. Types of AI
3. Expert Systems
4. Lesson Summary
5. Learning Outcomes
6. Quiz 1.11
7. Reference Videos
1. Introduction
1. Siri and Alexa
2. What Is an Intelligent Agent?
3. Agent Classes
4. Lesson Summary
5. Quiz 2.1
6. Reference Videos
1. Intelligent Agents
2. Simple Reflex Agent
3. Condition-Action Rule
4. Simple Reflex Agent Example
5. Lesson Summary
6. Quiz 2.2
1. Intelligent Agents
2. Model-Based Reflex Agents
3. Model-Based Cleaning
4. Lesson Summary
5. Quiz 2.3
1. Goal-Setting
2. Defining Goal-Based Agents
3. Goal-Based Agent Expansion
4. A Goal-Based Example
5. Lesson Summary
6. Quiz 2.4
1. Re-Routing
2. Utility-Based Agent
3. Utility-Based Agent Characteristics
4. Making Decisions
5. Lesson Summary
6. Quiz 2.5
1. Learning Useful Information
2. What Are Learning Agents?
3. Learning Agent Components
4. Application
5. Lesson Summary
6. Quiz 2.6
1. Introduction
1. Why Should I Write Pseudocode?
2. How Do I Write Pseudocode?
3. Examples of the Pseudocode
4. Lesson Summary
5. Key Terms
6. Learning Outcomes
7. Quiz 3.1
1. Steps to Writing a Program
2. Writing Code
3. Syntax
4. Online Compilers
5. Testing
6. Debugging
7. Lesson Summary
8. Learning Outcomes
9. Quiz 3.2
1. What Is a Computer Security Risk?
2. Viruses, Worms, & Ransomware
3. Spyware & Trojan Horses
4. Misconfiguration
5. Unsafe Computing Habits
6. Lesson Summary
7. Quiz 3.3
1. Looking Ahead
2. Machine Learning
3. Techniques
4. Applications
5. Lesson Summary
6. Quiz 3.4
1. Programming Languages
2. Low-Level Languages
3. High-Level Languages
4. Compiler
5. Interpreter
6. Comparison
7. Compiled vs. Interpreted
8. Lesson Summary
9. Learning Outcomes
10. Quiz 3.5
1. Making Measurements
2. Example Measurements
3. Calculations with Measurements
4. Lesson Summary
5. Learning Outcomes
6. Quiz 3.6
1. Bayes' Theorem
2. Generalizing Bayes' Theorem
3. Revising probability values to make decisions
4. Restating Bayes' Theorem
5. Updating the Probabilities
6. Bayesian Inference in Artificial Intelligence
7. Lesson Summary
8. Quiz 3.7
1. Heuristics
2. Nearest Neighbor
3. Alpha-Beta Pruning
4. Lesson Summary
5. Quiz 3.8
1. Importance of Searching
2. Search Visualization
3. Search Types in AI
4. Lesson Summary
5. Quiz 3.9
1. Rules of the Game
2. What is Game Theory?
3. How is Game Theory connected to Artificial Intelligence?
4. Algorithms for Decision-Making
5. Lesson Summary
6. Quiz 3.10
1. Lesson Overview and Knowledge Required
2. Program Code
3. Code Application
4. Follow-Up Questions
5. Answer Key
1. Introduction
1. Definition: Constraint Satisfaction Problem
2. Popular Problems with CSP
3. Converting Process & Example
4. Example Problem (Map Coloring)
5. Declare Variable Set, Domain Set and Constraint Set
6. Inferences
7. Lesson Summary
8. Quiz 4.1
1. Bayes Theorem and Bayesian Networks - A Quick Recap
2. What is a Bayesian Software?
3. Applications of Bayesian Software
4. Quiz 4.2
1. The Self-Learning Machine
2. What Does Neural Mean?
3. Types of Neural Networks
4. Word Vector Representations
5. Lesson Summary
6. Quiz 4.3
1. Getting More from Your Data
2. What is a Decision Tree?
3. What is the Decision Tree Algorithm?
4. What is Data Mining?
5. How is the Decision Tree Algorithm Used in Data Mining?
6. Lesson Summary
7. Quiz 4.4
1. The Markov Decision Process
2. Building a Process Example
3. Markov Decision Process Properties
4. Markov Decision Process States
5. Example Execution
6. Lesson Summary
7. Quiz 4.5
1. Definition
2. Propositional Logic
3. Logical Connectors
4. Truth Tables
5. Forming propositional logic using sentences provided
6. First Order Logic (FOL)
7. Representing simple statements in FOL
8. Application of Computational Logic in Artificial Intelligence
9. Quiz 4.6
1. Definition
2. History
3. Solutions to SLAM problem
4. Basic Implementation and working
5. Importance in Augmented Reality (AR) and Artificial Intelligence (AI)
6. Some applications
7. Challenges
8. Lesson Summary
9. Quiz 4.7
1. Daring to Dream
2. What Is Artificial Intelligence?
3. What Is LISP?
4. LISP & Artificial Intelligence
5. Lesson Summary
6. Quiz 4.8
1. Introduction
1. Logic
2. Propositions
3. True or False
4. Critical Thinking
5. Lesson Summary
6. Learning Outcomes
7. Quiz 5.1
1. Propositions
2. Negation
3. Truth Value
4. Truth Table
5. Lesson Summary
6. Learning Outcomes
7. Quiz 5.2
1. Limitation of Propositional Theory and need for First Order Logic
2. Definition
3. Representing simple statements in FOL
4. Quantifiers
5. Representing statements in FOL using Quantifiers
6. Representing statements in FOL using Quantifiers - Worked out Examples
7. First Order Logic in AI
8. Lesson Summary
9. Quiz 5.3
1. Definition
2. Propositional variables
3. Logical Connectives
4. Truth Tables
5. Forming propositional logic using sentences provided
6. Lesson Summary
7. Quiz 5.4
1. What Is Knowledge Engineering?
2. Knowledge Engineering: Processes
3. The Building Blocks of AI
4. Role of the Knowledge Engineer
5. Knowledge Engineering: Examples
6. Lesson Summary
7. Quiz 5.5
1. Artificial Intelligence
2. Forward Chaining
3. Efficiency & Inefficiency
4. Lesson Summary
5. Quiz 5.6
1. Artificial Intelligence
2. Backward Chaining
3. Efficiency & Inefficiency
4. Lesson Summary
5. Quiz 5.7
1. The Morning Routine
2. What is Artificial Intelligence?
3. Using Prolog in Artificial Intelligence
4. Facts, Rules, and Queries in Prolog
5. Artificial Intelligence in Use Today
6. Lesson Summary
7. Quiz 5.8
1. Lesson Overview & Knowledge Required
2. Program Code
3. Code Application
4. Follow-Up Questions
5. Answer Key
1. Introduction
1. Learning from Labeled Data
2. Supervised Learning
3. Linear Regression
4. Classification
5. Lesson Summary
6. Quiz 6.1
1. Unsupervised Learning Machines
2. Are There Downsides?
3. The Most Common Clustering Algorithm
4. A Real World Example
5. Lesson Summary
6. Quiz 6.2
1. Natural Language Processing (NLP)
2. Machine Learning (ML)
3. Deep Learning (DL)
4. Lesson Summary
5. QUIZ 6.3
1. Introduction to Entropy
2. What Is Entropy?
3. What Is Machine Learning?
4. Entropy and Machine Learning
5. Lesson Summary
6. Reference Videos
7. Quiz 6.4
1. Google Maps and Navigation
2. What Is a Decision Network?
3. Evaluation of Decision Network by an AI Agent
4. A Real World Example
5. Nodes Types
6. Lesson Summary
7. Quiz 6.5
1. Definition
2. History
3. Applications in Classification Problems
4. Structural Risk Minimization (SRM)
5. Lesson Summary
6. Quiz 6.6
1. Probability—A Quick Recap
2. Conditional Probability
3. Bayes' Theorem
4. Probabilistic Reasoning
5. Bayesian Network
6. Bayesian Network—Example
7. Lesson Summary
8. Quiz 6.7
1. Definition
2. Reasons to use Probabilistic Reasoning in AI
3. Notations and Properties
4. Conditional Property
5. Bayes' Theorem
6. Bayesian Network
7. Bayesian Network - Example
8. Effect of time on Probabilistic Reasoning
9. Lesson Summary
10. Quiz 6.8
1. What is Speech Recognition?
2. How Speech Recognition Works in Artificial Intelligence Systems
3. Language Models
4. Lesson Summary
5. Quiz 6.9
1. What is Computer Vision?
2. How Computer Vision Works
3. Deep Learning
4. Layers
5. Training
6. Classifying Objects
7. Debugging
8. Lesson Summary
9. Quiz 6.10
1. Lesson Overview & Knowledge Required
2. Program Code
3. Create a Custom Header File
4. Create the Main File
5. Show Message
6. Get Phrase
7. Learner Function
8. Compile and Run!
9. Code Application
10. Follow-Up Questions
11. Answer Key
12. Follow-Up Questions
1. Introduction
1. Ever Feel Like a Robot?
2. Robot Creation
3. Planning the Robot Environment
4. Motion planning
5. Motion algorithms
6. Designing the Robot
7. Physics-Based Modeling and Simulation
8. Teaching a Robot
9. Lesson Summary
10. Quiz 7.1
1. Ethics and Artificial Intelligence
2. Ethics, AI, and Economics
3. Lesson Summary
4. Quiz 7.2
1. Artificial Intelligence
2. AI in Everyday Life
3. The Future of Artificial Intelligence
4. Lesson Summary
5. Quiz 7.3
1. Advancement of Healthcare
2. A.I. in Decision Making
3. Order Entry
4. Behavior Changes
5. Adapting to Change
6. Lesson Summary
7. Quiz 7.4
1. Artificial Intelligence(AI) Meets Manufacturing
2. How AI Can Benefit Manufacturing
3. Adaptive Manufacturing
4. The Cobot-Human Relationship
5. Benefits of Cobots in Manufacturing
6. Lesson Summary
7. Quiz 7.5
1. AI and Advanced Robotics
2. Before Smart Devices
3. Recreating the Human Experience
4. Interconnected Lives
5. Lesson Summary
6. Quiz 7.6
1. Artificial Intelligence Everywhere
2. The Cashless Society
3. Employment
4. Data Ownership
5. AI in Business and the Global Economy
6. Lesson Summary
7. Quiz 7.7
1. The Future Is Already Here
2. Is This Good or Bad?
3. A Different Way of Thinking
4. Should We Be Worried?
5. Lesson Summary
6. Quiz 7.8
1. Impact of AI and Robotics on our Future
2. Impacts of AI and Robotics in the Industry
3. Pro and Cons of AI and Robotics
4. Governance Models
5. Lesson Summary
6. Quiz 7.9
1. Assignment
2. FINAL EXAM

About this course

449 lessons
1750+ Transfer Colleges
ACE & NCCRS Approved
Globally Recognized

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25% Assignments

25% Final Exam

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CHAPTER 1 - Fundamentals of Artificial Intelligence

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

Ch 1.2 - Types of Artificial Intelligence

Ch 1.3 - Automated Online Search Tools & Intelligent Agents

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

Ch 1.5 - How to Evaluate Logarithms

Ch 1.6 - Basic Probability Theory: Rules & Formulas

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

Ch 1.8 - Machine Learning vs. Artificial Intelligence

Ch 1.9 - Perkins' Theory of Learnable Intelligence

Ch 1.10 - Cognitive Function: Definition & Assessment

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

CHAPTER 2 - Intelligent Agents

Ch 2.1 - Intelligent Agents: Definition, Types & Examples

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

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

Ch 2.4 - Goal-based Agents: Definition & Examples

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

Ch 2.6 - Learning Agents: Definition, Components & Examples

CHAPTER 3 - Using Artificial Intelligence in Searches

Ch 3.1 - Writing Pseudocode: Algorithms & Examples

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

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

Ch 3.4 - Machine Learning: Techniques & Applications

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

Ch 3.6 - Measurements & Uncertainty in Science

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

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

Ch 3.9 - Uninformed & Adversarial Searches in AI

Ch 3.10 - Game Theory in Artificial Intelligence

Ch 3.11 - Practical Application for Artificial Intelligence: AI Searches

CHAPTER 4 - Constraint Satisfaction in Artificial Intelligence

Ch 4.1 - Constraint Satisfaction Problems: Definition & Examples

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

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

Ch 4.4 - Decision Tree Algorithm in Data Mining

Ch 4,5 - Markov Decision Processes: Definition & Uses

Ch 4.6 - Computational Logic: Methods & AI Applications

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

Ch 4.8 - What is LISP in Artificial Intelligence?

CHAPTER 5 - Logical Agents & First-Order Logic

Ch 5.1 - Critical Thinking and Logic in Mathematics

Ch 5.2 - Propositions, Truth Values and Truth Tables

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

Ch 5.4 - Propositional Logic Algorithms: Definition & Types

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

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

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

Ch 5.8 - Prolog in AI: Definition & Uses

Ch 5.9 - Practical Application for Artificial Intelligence: Backward Chaining

CHAPTER 6 - Learning & Reasoning in Artificial Intelligence

Ch 6.1 - Supervised Learning in Machine Learning

Ch 6.2 - Unsupervised Learning in Machine Learning

Ch 6.3 - Natural Language Processing & Deep Learning

Ch 6.4 - Entropy in Machine Learning

Ch 6.5 - Decision Networks in Artificial Intelligence: Nodes & Uses

Ch 6.6 - Support Vector Machines (SVMs): Definitions & Applications

Ch 6.7 - Probabilistic Reasoning & Artificial Intelligence

Ch 6.8 - Probabilistic Reasoning Over Time in AI

Ch 6.9 - Speech Recognition in Artificial Intelligence

Ch 6.10 - Computer Vision & Image Classification in AI

Ch 6.11 - Practical Application for Artificial Intelligence: Learning Chatbot

CHAPTER 7 -The Present & Future of Artificial Intelligence

Ch 7.1 - Programming Articulated Robots: Design, Planning & Control

Ch 7.2 - Ethics of Artificial Intelligence

Ch 7.3 - Artificial Intelligence in Everyday Life

Ch 7.4 - Artificial Intelligence Uses in Health Care & Medicine

Ch 7.5 - Artificial Intelligence Uses in Manufacturing

Ch 7.6 - The Future of AI & Advanced Robotics: Scalability

Ch 7.7 - AI & the Economy of the Future

Ch 7.8 - Robots & the Future Workplace

Ch 7.9 - The Future of AI: Impacts on Industry, Governments & Society

FINAL EXAM & Assignment

About this course

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