Artificial Intelligence

Massachusetts Institute of Technology via MIT OpenCourseWare

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Details

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Provider

MIT OpenCourseWare
Pricing

Free Video
Languages

English
Effort

24 hours
Sessions

Self-Paced
Level

Intermediate

Found in

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Syllabus

1. Introduction and Scope.
2. Reasoning: Goal Trees and Problem Solving.
3. Reasoning: Goal Trees and Rule-Based Expert Systems.
4. Search: Depth-First, Hill Climbing, Beam.
5. Search: Optimal, Branch and Bound, A*.
6. Search: Games, Minimax, and Alpha-Beta.
7. Constraints: Interpreting Line Drawings.
8. Constraints: Search, Domain Reduction.
9. Constraints: Visual Object Recognition.
10. Introduction to Learning, Nearest Neighbors.
11. Learning: Identification Trees, Disorder.
12a: Neural Nets.
12b: Deep Neural Nets.
13. Learning: Genetic Algorithms.
14. Learning: Sparse Spaces, Phonology.
15. Learning: Near Misses, Felicity Conditions.
16. Learning: Support Vector Machines.
17. Learning: Boosting.
18. Representations: Classes, Trajectories, Transitions.
19. Architectures: GPS, SOAR, Subsumption, Society of Mind.
21. Probabilistic Inference I.
22. Probabilistic Inference II.
23. Model Merging, Cross-Modal Coupling, Course Summary.
Mega-R1. Rule-Based Systems.
Mega-R2. Basic Search, Optimal Search.
Mega-R3. Games, Minimax, Alpha-Beta.
Mega-R4. Neural Nets.
Mega-R5. Support Vector Machines.
Mega-R6. Boosting.
Mega-R7. Near Misses, Arch Learning.

Taught by

Prof. Patrick Henry Winston

Reviews

4.7 rating, based on 239 Class Central reviews

Start your review of Artificial Intelligence

Meiyarasan @MEIYARASAN

learning artificial intelligence (AI) prefect for beginners to intermediate learners.
you can use this as a study guide , revision sheet or presentation base.
main goals of AI:
perception (e.g., vision,speech)
reasoning and problem solving
learning freom data
interacting with the environment
Shahin Ahammed (EAGLE) @sin9

Just completed this AI course. It was clear, practical, and helped me understand AI concepts better.
Luc Xavier Fabrice Cornet @Xavier20

Though a bulky course I appreciate every bit of it.One of the toughest explanation made easy by the MIT team. I will not miss any further opportunity to tune other interesting course.
Gaspar Adriano Mateus João @gasparEng1neer

Muito Obrigado, a universidade de Harvard, pela oportunidade que me foi concedida pelo privilégio de ter participado no Curso de Inteligencia Artificial, e pela forma como o professor Mr. Patrick Henry Winston, desenvolveu os Temas, tirei muito proveito, acima de tudo, sinto e tenho a plena confiança que estou capacitado, preparado para enfrentar os vários desafios que provavelmente irão surgir, no mercado Internacional concernente a AI.
Marta Reinhardt @martareinhardt

Resultado prático pra mim: depois de terminar, consegui aplicar tudo no meu trabalho/projeto pessoal e já vi diferença real (melhor entendimento de modelos, código mais limpo, confiança pra encarar problemas novos). Nota 10/10 no custo-benefício!
Awe George Tobiloba @atmaaggeorge

A very good crash course.
I have been able to learn AI under Games, probability, real life variations and lots more.
Thank you for the crash course.
It's one of the best so far.
Mohammed Akram @Akramin

well explained in detail. the thought process very well delivered and well presented. It was a nice learning even though the video is quite old, it made the understanding clearly. Thank you
BISWAS BASYAL @biswas135

The Artificial Intelligence course from MIT via OpenCourseWare offers a comprehensive introduction to key AI concepts, including machine learning, reasoning, and natural language processing. The course content is well-organized, with clear lectures…

The Artificial Intelligence course from MIT via OpenCourseWare offers a comprehensive introduction to key AI concepts, including machine learning, reasoning, and natural language processing. The course content is well-organized, with clear lectures and high-quality readings that dive deep into theoretical and practical aspects of AI. The assignments are thought-provoking, pushing students to apply their knowledge to solve real-world problems, although they can be challenging for those without prior experience.

The learning experience is self-paced, which makes it accessible to a wide range of learners, but the lack of direct interaction with instructors or peers can be a drawback. While the material is rich and well-structured, some students may struggle if they do not have a solid foundation in programming or mathematics. However, the quality of content and the opportunity to learn from one of the world's leading institutions more than compensates for this.

Overall, the course provides valuable insights into the fundamentals of AI and is highly recommended for those looking to deepen their understanding of the subject. Although challenging, the course materials are robust and thorough, making it an excellent resource for both academic and practical applications in AI.
Muhammad Hassaan Mughal

Before this course, I thought AI was limited to neural networks and ChatGPT. Now I understand how much history and breadth there is. The beginning of goal trees and rule-based systems demonstrated the classic symbolic approach of attempting to codi…

Before this course, I thought AI was limited to neural networks and ChatGPT. Now I understand how much history and breadth there is.

The beginning of goal trees and rule-based systems demonstrated the classic symbolic approach of attempting to codify human expertise into rules. It makes sense, but it also fails easily.

The most difficult but fascinating aspect was the search. Learning how simple concepts like depth-first, hill climbing, A*, and minimax with alpha-beta can solve puzzles and play chess taught me that a lot of intelligence is simply efficient searching.

Constraints and visual recognition were a surprise. I never considered that understanding a cube's line drawing could be a constraint problem.

My favorite part was the learning section, which covered nearest neighbors, identification trees, neural nets, deep nets, SVMs, and boosting. It clearly shows a progression from memorizing data to actually generalizing.

In the end, the course changed my perspective. Artificial intelligence is not magic. It combines search, knowledge representation, example-based learning, and uncertainty handling.
Vitalii Pomarnatskyi @earlybird95

I really enjoyed this course. It provided a clear and practical overview of AI fundamentals. The instructor explained complex topics in an easy-to-understand way, and the examples were very helpful. I now feel more confident about working with AI technologies.
Anurag Tewary

That was straight up awesome clear and grasp able. would like to continue with different cources as well.
Syed Zayed Hassan @syedzayedhassan

The Artificial Intelligence course from MIT OpenCourseWare is an excellent starting point for AI learners.
Professor Patrick Henry Winston delivers the lectures with clarity and depth.
It covers foundational AI concepts in a structured and engaging way.
The course balances theory with real-world relevance.
Highly recommended for anyone interested in understanding the core of AI.
Albertus Kevin Daniswara

This course provides an exceptional foundation in artificial intelligence. Patrick Winston's lectures make complex topics like search algorithms, neural nets, and representations intuitive and engaging. The materials from MIT OpenCourseWare are clear, comprehensive, and essential for anyone serious about understanding the core fundamentals of AI.
Emmanuel Chinonso Tv

The Artificial intelligence class was valuable and eye opening learning experience it gave me a better understanding of what Ai is how it works and how it is changing the way people learn, work communicate and solve problems I particularly appreciated the practical relevance of the course because Ai can be a useful tool in a education research and Christian ministry when use wisely and responsibly the class encouraged me to think more critical about both opportunities and challenges that come with Ai
Talles Hp

This AI course was very informative and practical. It helped me understand the fundamentals of Artificial Intelligence and how AI can be applied in real-world situations. The content was clear, well organized, and useful.
TEWODROS TADESSE FENTAW

It is a mandatory and good technology courses of taking the list getting a new things were formed the putting of ready.
Sarim

This course is very useful for me, I thank to everyone who supported me, thank you all of you. It's my pleasure to learn you. I am very happy and I think this will also inherited to the others. May God bless on you that you are very dedicated to teach us
Weslly Oliveira

Curso ótimo, aprendi bastante, antes tinha duvidas e agora não mais, vale muito a pena, mesmo sem tempo, consegui assistir todas as aulas
Saikrishna Chinthala

This course provides a solid introduction to artificial intelligence concepts with clear explanations and challenging problem sets. It covers important topics such as search algorithms, machine learning, and reasoning. The material is rigorous and requires consistent effort, but it is highly rewarding for anyone interested in understanding AI fundamentals. I would recommend it to students who want a strong academic foundation in AI.
Fabiano Araújo Negreiros

Este curso oferece uma excelente introdução aos fundamentos da Inteligência Artificial. O professor explica conceitos complexos de forma clara, utilizando exemplos práticos que facilitam o entendimento. Mesmo sendo um curso de 2010, o conteúdo continua extremamente relevante para compreender a base da IA moderna, incluindo técnicas de busca, raciocínio, representação do conhecimento e aprendizado. Recomendo para estudantes, pesquisadores e profissionais que desejam construir uma base sólida antes de estudar Deep Learning e IA generativa.