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Artificial Intelligence: Logic, Learning, and Problem Solving

Master AI logic, learning, and problem-solving. Develop robust intelligent systems with practical, hands-on training.

(INTRO-AI.AU1) / ISBN : 979-8-90059-110-0
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About This Course

This Artificial Intelligence course online provides a rigorous foundation in AI logic, learning, and problem-solving. You'll tackle core concepts from propositional and first-order logic to advanced search algorithms, machine learning, and neural networks. With 14 hands-on labs and over 15 hours of video lessons, you'll gain practical experience in building intelligent systems. Understand that while AI offers powerful solutions, every design involves trade-offs between computational complexity and solution optimality. This isn't about magic; it's about engineering intelligent behavior through structured reasoning and adaptive learning.

Skills You’ll Get

  • Logic-Based AI Systems: Master propositional and first-order logic for knowledge representation and inference, understanding their inherent limitations in real-world complexity and decidability.
  • Algorithmic Problem Solving: Implement and analyze search algorithms (uninformed, heuristic) and game theory to navigate complex state spaces, recognizing the trade-off between optimality and computational cost.
  • Machine Learning & Neural Networks: Apply foundational machine learning techniques like perceptrons, nearest neighbor methods, and backpropagation for data classification and pattern recognition, acknowledging data quality as a critical constraint.
  • Reinforcement Learning: Design and simulate agents that learn optimal policies through interaction, understanding the challenge of balancing exploration versus exploitation in dynamic, uncertain environments.
Download Course Outline

1

Introduction

  • What is Artificial Intelligence?
  • The History of AI
  • AI and Society
  • Agents
  • Knowledge-Based Systems
  • Exercises
2

Propositional Logic

  • Syntax
  • Semantics
  • Proof Systems
  • Resolution
  • Horn Clauses
  • Computability and Complexity
  • Applications and Limitations
  • Exercises
3

First-Order Predicate Logic

  • Syntax
  • Semantics
  • Quantifiers and Normal Forms
  • Proof Calculi
  • Resolution
  • Automated Theorem Provers
  • Mathematical Examples
  • Applications
  • Summary
  • Exercises
4

Limitations of Logic

  • The Search Space Problem
  • Decidability and Incompleteness
  • The Flying Penguin
  • Modeling Uncertainty
  • Exercises
5

Logic Programming with PROLOG

  • PROLOG Systems and Implementations
  • Simple Examples
  • Execution Control and Procedural Elements
  • Lists
  • Self-modifying Programs
  • A Planning Example
  • Constraint Logic Programming
  • Knowledge Check
  • Summary
  • Exercises
6

Search, Games and Problem Solving

  • Introduction
  • Uninformed Search
  • Heuristic Search
  • Games with Opponents
  • Heuristic Evaluation Functions
  • State of the Art
  • Exercises
7

Reasoning with Uncertainty

  • Computing with Probabilities
  • The Principle of Maximum Entropy
  • Lexmed, an Expert System for Diagnosing Appendicitis
  • Reasoning with Bayesian Networks
  • Summary
  • Exercises
8

Machine Learning and Data Mining

  • Data Analysis
  • The Perceptron, a Linear Classifier
  • Nearest Neighbor Methods
  • Data Normalization
  • Quality Metrics for Classifiers
  • Decision Tree Learning
  • Cross-Validation and Overfitting
  • Data Augmentation
  • Learning of Bayesian Networks
  • The Naive Bayes Classifier
  • One-Class Learning
  • Clustering
  • Data Mining in Practice
  • Summary
  • Exercises
9

Neural Networks

  • From Biology to Simulation
  • Hopfield Networks
  • Neural Associative Memory
  • Linear Networks with Minimal Errors
  • The Backpropagation Algorithm
  • Deep Learning
  • Creativity
  • Transformers Take Over Natural Language Processing
  • Support Vector Machines
  • Summary and Outlook
  • Exercises
10

Reinforcement Learning

  • Introduction
  • The Task
  • Uninformed Combinatorial Search
  • Value Iteration and Dynamic Programming
  • A Learning Walking Robot and Its Simulation
  • Q-Learning
  • Exploration and Exploitation
  • Approximation, Generalization, and Convergence
  • Applications
  • AlphaGo, the Breakthrough in Go
  • Curse of Dimensionality
  • Summary and Outlook
  • Exercises

1

Introduction

  • Understanding AI
2

Propositional Logic

  • Converting Logical Expression into CNF
  • Understanding CNF and Resolution
  • Converting the Horn Clause into Implication Form
3

First-Order Predicate Logic

  • Converting Sentences into FOL Form
4

Limitations of Logic

  • Understanding the Logical Limits of AI
5

Logic Programming with PROLOG

  • Understanding Logic Programming with PROLOG
6

Search, Games and Problem Solving

  • Exploring Game Strategies in AI
7

Reasoning with Uncertainty

  • Calculating the Conditional Probability
  • Understanding Independent Events
8

Machine Learning and Data Mining

  • Computing Min-Max Normalization and Standardization (Z-Score)
  • Evaluating Binary Classifiers
9

Neural Networks

  • Calculating the Total Squared Error
10

Reinforcement Learning

  • Understanding Reinforcement Learning

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This Artificial Intelligence Logic training online covers propositional logic, first-order predicate logic, and their respective proof systems like resolution. We also critically examine the inherent limitations of logic, such as the search space problem, decidability, and incompleteness, which are crucial for understanding real-world AI system constraints.

Highly practical. The course integrates 14 hands-on labs and 50 practice exercises, focusing on implementing search algorithms, logic programming with PROLOG, and applying machine learning techniques. You'll learn to approach problems like game playing and robotic control, understanding that real-world solutions often require balancing computational resources against desired performance.

Yes, programming is integral. The course includes dedicated sections on Logic Programming with PROLOG, providing practical experience in declarative programming for AI. While PROLOG is a focus, the algorithmic concepts for search, machine learning, and reinforcement learning are universally applicable and can be translated to other languages.

This course provides a solid foundation in machine learning and neural networks. You'll learn about data analysis, linear classifiers like the Perceptron, nearest neighbor methods, and the Backpropagation algorithm for neural networks. It's designed to build a strong conceptual and practical understanding, preparing you for more advanced topics, but it's not a deep learning specialization.

  You'll get access to 14 hands-on labs, 10 video lessons totaling over 15 hours, 94 practice quizzes, 100 flashcards, 50 practice exercises, and 10 comprehensive chapters. These resources are designed to reinforce your understanding of AI logic and reasoning, ensuring a robust learning experience.

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