Topic Name Description
Page Course Syllabus
1.1: The Turing Test Page The Turing Test for Intelligence
Page Why the Turing Test Is Important
1.2: The Four Types of AI Page Is Intelligence How You Think or the Output of Thinking?
2.1: Introduction to Agent-Based AI Page Agents, Agent Types, and Their Capabilities
2.2: Analyzing Environmental Characteristics Page Properties of Problem Environments and How to Analyze Them
3.1: Learning in AI and Agents Book Supervised, Unsupervised, and Reinforcement ML
3.2: Applications of ML in Neural Networks Page Newer Machine Learning Models and Applications
4.1: Classification Algorithms Page Classification versus Regression
Page Importance of Classification and Regression in Machine Learning
Page Classification Using K-nearest Neighbors Algorithm
4.2: Classification Algorithm Performance Page False Positives / False Negatives / Confusion Matrix
Page Precision and Recall Calculations from Confusion Matrix
Page Linear Regression – How It Works
4.3: Linear Regression Algorithms Page Metrics for Linear Regression Effectiveness: R-squared, MSE and RSE
Page Lasso and Ridge Regression
Page Improving Linear Regression by Reducing Residual Errors
4.4: Other Supervised ML Classification Algorithms Page Classification Using Decision Trees
Page Classification Using Logistic Regression
Page Applying Bayes' Theorem in Machine Learning
4.5: Unsupervised Learning and Reinforcement Learning Page Unlabelled Data and Unsupervised Machine Learning
Page Principles and Applications of Reinforcement Learning
4.6: ML Using Neural Networks Page Introduction to Neural Networks Basics
Book Neural Networks: Types and Applications
5.1: Integrating ML Skills Page Applying Classification to Determine Insurability
Book How Regression Is Applied in Contemporary Computing
Page Using Neural Networks in Cancer Detection
5.2: General AI Problem-Solver Architecture Page Characteristics of General Problem-Solver
5.3: Designing a General Problem-Solving Agent Page How GPS Is Used
Page Computational Tractability of GPS
6.1: Uninformed Search Algorithms Page Uninformed or Brute Force Search
Page Depth First Search Algorithm
Page Breadth First Search Algorithm
Page Uniform Cost Search Algorithm
6.2: Heuristic Search Algorithms Page Heuristics and Using Them to Improve Search
Page Overview of A* Search and Analysis of Performance
7.1: Using Iterative Improvement to Solve Problems Book Iterative Improvement Algorithms and Hill-Climbing
Page Constraint Satisfaction Problems and Their Importance
7.2: Improving Algorithm Efficiency Page How Simulated Annealing Improves Hill-Climbing
Page Improving Mediocre Solutions Using Genetic Algorithms
8.1: Game Trees and the Minimax Algorithm Page Principles of Game Trees and How to Create One
Page Using the Minimax Algorithm in Adversarial Games
Page Assumptions Underlying Minimax Approach
8.2: Game-Playing Strategies Page The Alpha Beta Pruning Algorithm
Book Tackling Multi-person Games
9.1: Foundations of NLP Page NLP Overview, Challenges, and Applications
9.2: How NLP Is Used Page Formal Analysis of Natural Language Structures
Page Topic Extraction from Long Text
Page Sentiment Analysis
9.3: NLP Models and Methods Book Long Short-Term Memory Models
Page Statistical Methods in NLP
9.4: Generative AI Page What Is Generative AI?
Page How Generative AI Works
Page Popular Generative AI Tools
10.1: Propositional Logic Page Propositional Logic
Book Using PL to Describe Properties of Systems
10.2: First Order Logic Page First Order Logic (FOL)
Book Using FOL to Describe the Properties of Systems
10.3: Bayesian Reasoning and Uncertainty Book Conditional Probability
Book Applying Bayes' Theorem in Deduction
10.4: Modeling Causality with Bayesian Networks Book Bayesian Networks
Page Markov Chains

Markov chains are one of the most common formalisms to describe event probabilities within a system where the next state is determined only by the current state but not by how the current state was achieved.
Book Applications of Hidden Markov Chains
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