Free Course Image Fundamentals of Artificial Intelligence

Free online courseFundamentals of Artificial Intelligence

Duration of the online course: 25 hours and 26 minutes

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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

Course Description

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?

This free course includes:

25 hours and 26 minutes of online video course

Digital certificate of course completion (Free)

Exercises to train your knowledge

100% free, from content to certificate

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Course comments: Fundamentals of Artificial Intelligence

PK

Prasanth Kalavakuri

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this is very useful course

AK

Aakash Kumar

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IITian teacher are great and telling about next level of idea.

AD

Aditi Dwivedi

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nice

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