What AI search algorithms are covered in the Fundamentals of Artificial Intelligence course?

The course covers state-space search, uninformed and informed search, heuristic search, constraint satisfaction, AND/OR graph search, and the AO* algorithm.

Does this artificial intelligence course explain minimax and alpha-beta pruning?

Yes. It introduces AI game playing, minimax game-tree evaluation, and alpha-beta pruning to reduce unnecessary search.

What knowledge representation and uncertainty topics are included in this AI course?

Topics include propositional and first-order logic, inference and resolution, answer extraction, procedural reasoning, Bayes’ rule, and Bayesian networks.

Free Course Image Fundamentals of Artificial Intelligence

Free online course Fundamentals of Artificial Intelligence

Duration of the online course: 25 hours and 26 minutes

4.6

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Build real AI skills with a free online course: search, heuristics, logic, and uncertainty—ideal for launching projects or preparing for interviews.

In this free course, learn about

Core AI concepts and the four dimensions for characterizing AI systems
AI techniques and problem formulation as state-space search
Uninformed search methods: characteristics, completeness, optimality, complexity
Heuristic functions and admissible heuristics; heuristic/informed search advantages
Constraint Satisfaction Problems (CSPs): variables, domains, constraints, solving basics
AND/OR graphs and AO* search: when used and why it improves on best-first search
Game-playing foundations: properties of board games as AI testbeds
Minimax decision-making and alpha-beta pruning to reduce game-tree evaluation
Knowledge representation: why formal languages are chosen for reasoning
Propositional logic: propositions, syntax/semantics, and basic reasoning
First-order logic: predicates, quantifiers, and expressiveness vs propositional logic
FOL inference with resolution: conversion to normal forms and theorem proving flow
Answer extraction and procedural control strategies in resolution-based reasoning
Uncertainty reasoning: Bayes' rule and Bayesian networks for probabilistic inference

About the free online course

Artificial Intelligence is no longer just a buzzword; it is a set of practical ideas that help computers reason, search for solutions, and make decisions when information is incomplete. This free online course is designed to give you a solid foundation in AI concepts that still power modern applications, from planning and optimization to reasoning systems and probabilistic models. If you are starting in technology or programming and want a clear pathway into Artificial Intelligence and Machine Learning, this course helps you understand how AI works under the hood, not just how to use ready-made tools.

You will learn to frame real problems in a way an AI system can solve, modeling them as state spaces and exploring different strategies to find solutions efficiently. You will see why some approaches rely on little knowledge beyond the problem definition, while others gain speed by using heuristics and informed guidance. Along the way, you will strengthen your intuition for how AI chooses actions, balances trade-offs such as time and memory, and avoids unnecessary work when navigating large search spaces.

The course also builds the reasoning side of AI, showing how knowledge can be represented with formal languages and how logical inference can produce reliable conclusions. You will move from basic logical statements to more expressive structures that can describe objects, relationships, and rules. You will also discover how automated reasoning works in practice, including how systems derive answers, manage inference steps, and apply procedural control to make reasoning tractable.

Because real-world data is often noisy or incomplete, you will study reasoning under uncertainty and learn how probabilistic thinking helps AI make better decisions. By understanding Bayes rule and the intuition behind Bayesian networks, you will be able to interpret uncertainty, combine evidence, and model dependencies in a way that is both structured and explainable. The included exercises help reinforce each topic, so you can confidently connect theory to problem-solving and build a strong base for future learning in machine learning, data science, or intelligent systems development.

Course content

Video class: Fundamentals of Artificial Intelligence [Introduction] 04m
Video class: Lec 01: Introduction to AI 35m
Exercise: What are the four dimensions in which artificial intelligence systems can be understood according to the lecture content?

Video class: Lec 02: Problem Solving as State Space Search 57m
Exercise: Which of the following statements best describes an AI technique?
Video class: Lec 03: Uniformed Search 47m
Exercise: What is the primary characteristic of uninformed search strategies in the context of artificial intelligence problem-solving as state space search?
Video class: Lec 04: Heuristic Search 35m
Exercise: What is an admissible heuristic in the context of heuristic functions in AI search?
Video class: Lec 05: Informed Search 46m
Exercise: What is the main advantage of informed search strategies over uninformed search strategies in artificial intelligence?
Video class: Lec 06: Constraint Satisfaction Problems 1h06m
Exercise: Which of the following best describes a Constraint Satisfaction Problem (CSP) in the context of Artificial Intelligence?
Video class: Lec 07: Searching AND/OR Graphs 40m
Exercise: What is the primary advantage of using AO* algorithm for searching AND-OR graphs compared to best-first search?

Video class: Lec 08: Game Playing 44m
Exercise: Which of the following is a characteristic of board games used in artificial intelligence research?
Video class: Lec 09: Minimax Alpha-Beta 42m
Exercise: What is the primary purpose of the alpha-beta pruning algorithm when applied to the minimax algorithm in game tree evaluation?

Video class: Lec 10: Introduction to Knowledge Representation 36m
Exercise: What is the foundational choice of a formal language for knowledge representation and reasoning in AI, and why is it chosen?
Video class: Lec 11: Propositional Logic 34m
Exercise: Which statement is an example of a proposition in propositional logic?
Video class: Lec 12: First Order Logic -I 45m
Exercise: Which of the following statements best describes the difference between propositional logic and first order logic?
Video class: Lec 13: First Order Logic -II 40m
Exercise: What makes first-order logic more expressive than propositional logic?
Video class: Lec 14: Inference in First Order Logic - I 53m
Exercise: Which of the following distinguishes first-order logic from higher-order logics?
Video class: Lec 15: Inference in FOL - II 59m
Exercise: In knowledge representation and reasoning, which process involves converting first order predicate calculus statements to a certain form before applying resolution?
Video class: Lec 16: Answer Extraction 33m
Exercise: In the context of knowledge representation and reasoning, what is the purpose of answer extraction in first order logic theorem proving systems?
Video class: Lec 17: Procedural Control of Reasoning 44m
Exercise: Which strategy in resolution-based reasoning involves evaluating the truth value of literals by attaching procedures for computation, rather than including these literals or their negations directly in the base set?

Video class: Lec 18: Reasoning under Uncertainty 43m
Exercise: What does the Bayes' rule allow an AI system to compute in terms of probabilistic inference?
Video class: Lec 19: Bayesian Network 47m
Exercise: What task does a Bayesian network primarily assist in when dealing with uncertainty in AI systems?
Video class: Lec 20: Decision Network 43m
Exercise: What is the main function of a decision network in the context of artificial intelligence?

Video class: Lec 21: Introduction to Planning 1h01m
Exercise: What is the primary advantage of using STRIPS representation for planning in artificial intelligence?
Video class: Lec 22: Plan Space Planning 41m
Exercise: What is a key advantage of partial order planning compared to total order planning?
Video class: Lec 23: Planning Graph and GraphPlan 45m
Exercise: What is a unique characteristic of the planning graph approach to planning compared to traditional state space or plan space search methods?
Video class: Lec 24: Practical Planning and Acting 51m
Exercise: In the context of artificial intelligence planning, what is a planning graph and how does it differ from the traditional state space for a planning problem?
Video class: Lec 25: Sequential Decision Problems 34m
Exercise: Which of the following best describes the concept of a planning graph in artificial intelligence planning?
Video class: Lec 26: Making Complex Decisions 36m
Exercise: Which of the following statements best describes the concept of a planning graph in artificial intelligence planning?

Video class: Lec 27: Introduction to Machine Learning 45m
Exercise: What is the primary difference between the state space representation and the planning graph in planning problems discussed in the module?
Video class: Lec 28: Learning Decision Trees 51m
Exercise: In the context of planning in artificial intelligence, what is a key feature of a planning graph?
Video class: Lec 29: Linear Regression 37m
Exercise: What is a distinctive feature of the planning graph in the Graphplan algorithm compared to traditional state space or plan space searches?
Video class: Lec 30: Support Vector Machines 42m
Exercise: Which of the following best describes the concept of a planning graph in the context of AI planning?
Video class: Lec 31: Unsupervised Learning 51m
Exercise: What is the primary distinction between a state space representation and a planning graph in the context of AI planning?
Video class: Lec 32: Reinforcement Learning 32m
Exercise: In the context of planning in artificial intelligence, which of the following statements about a planning graph is correct?
Video class: Lec 33: Learning in Neural Networks 38m
Exercise: In a planning graph, what do mutually exclusive (mutex) propositions and actions represent?
Video class: Lec 34: Deep Learning: A Brief Overview 53m
Exercise: Which statement best describes the concept of a planning graph in the context of artificial intelligence planning?