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Master the basics of AI with this free online course from NPTEL. Explore problem-solving, search techniques, game theory, logic, planning, machine learning, and more!
Welcome to the "Fundamentals of Artificial Intelligence" course, a comprehensive program designed to introduce students to the dynamic and evolving field of artificial intelligence (AI). Spanning a total duration of 25 hours and 26 minutes, this course delves deep into the core concepts, theories, and practical applications of AI, making it an essential resource for anyone interested in exploring the realms of intelligent systems and machine learning.
The course begins with an introductory lecture that sets the foundation for understanding the basics of artificial intelligence. Students will gain insight into what AI entails, its significance, and the various fields it encompasses. Moving forward, the course covers problem-solving techniques, starting with state space search and progressing through both uninformed and heuristic search methods. These lectures provide the tools needed to tackle complex problems by navigating through different states and employing efficient strategies.
As students advance, they will encounter topics such as constraint satisfaction problems and the intricacies of searching AND/OR graphs. The course also delves into game playing strategies, including the Minimax algorithm and alpha-beta pruning, which are fundamental concepts in strategic AI applications. Knowledge representation serves as another critical area, with lectures dedicated to propositional logic and first-order logic, which are crucial for representing and reasoning about knowledge in AI systems.
The course doesn't stop at theoretical knowledge. It incorporates practical aspects such as procedural control of reasoning and reasoning under uncertainty. Bayesian and decision networks are introduced to help students understand probabilistic models and decision-making processes in uncertain environments. Planning is another significant topic covered, with lectures on plan space planning, planning graphs, and practical planning and acting strategies. These lessons equip students to design and execute plans effectively in AI applications.
One of the highlights of the course is its focus on machine learning, a pivotal area in AI. Students will explore decision trees, linear regression, support vector machines, and unsupervised learning techniques. Reinforcement learning and learning in neural networks are also key components, providing insight into how machines can learn from experience and improve over time. The course concludes with an overview of deep learning, offering a glimpse into this cutting-edge area that powers many modern AI advancements.
Though this course is rich in content and intricately structured, no reviews are available yet, suggesting that feedback is awaited from future participants. These expert-curated lectures belong to the Information Technology category and specifically target the subcategory of Artificial Intelligence, making it an ideal pick for IT professionals, students, and enthusiasts aiming to advance their understanding and skills in AI.
Embark on this enlightening journey with the "Fundamentals of Artificial Intelligence" to unlock the doors to a world where machines think, learn, and make intelligent decisions.
Video class: Fundamentals of Artificial Intelligence [Introduction]
0h04m
Video class: Lec 01: Introduction to AI
0h35m
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
0h57m
Exercise: Which of the following statements best describes an AI technique?
Video class: Lec 03: Uniformed Search
0h47m
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
0h35m
Exercise: What is an admissible heuristic in the context of heuristic functions in AI search?
Video class: Lec 05: Informed Search
0h46m
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
0h40m
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
0h44m
Exercise: Which of the following is a characteristic of board games used in artificial intelligence research?
Video class: Lec 09: Minimax Alpha-Beta
0h42m
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
0h36m
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
0h34m
Exercise: Which statement is an example of a proposition in propositional logic?
Video class: Lec 12: First Order Logic -I
0h45m
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
0h40m
Exercise: What makes first-order logic more expressive than propositional logic?
Video class: Lec 14: Inference in First Order Logic - I
0h53m
Exercise: Which of the following distinguishes first-order logic from higher-order logics?
Video class: Lec 15: Inference in FOL - II
0h59m
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
0h33m
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
0h44m
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
0h43m
Exercise: What does the Bayes' rule allow an AI system to compute in terms of probabilistic inference?
Video class: Lec 19: Bayesian Network
0h47m
Exercise: What task does a Bayesian network primarily assist in when dealing with uncertainty in AI systems?
25 hours and 26 minutes of online video course
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