Artificial Intelligence (Fall 2010)

Massachusetts Institute of Technology via MIT OpenCourseWare

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Details

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Provider

MIT OpenCourseWare
Pricing

Free Online Course
Languages

English
Duration & workload

24 hours
Sessions

On-Demand

Found in

Overview

Course Features

Video lectures
Captions/transcript
Assignments: programming (no examples)
Exams (no solutions)
Recitation videos

Educator Features

Instructor insights
Teaching assistant insights

Course Highlights

This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.

Course Description

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

4.8 rating, based on 12 Class Central reviews

Start your review of Artificial Intelligence (Fall 2010)

Bharath Raj.A

I recently completed the AI course, and it exceeded my expectations. The content was comprehensive, providing a solid understanding of AI principles and practices. I appreciated the step-by-step approach, which made complex topics like machine learning and neural networks easier to grasp.
The instructors were engaging and knowledgeable, with clear explanations and practical examples. The interactive elements, like quizzes and assignments, reinforced my learning. I also enjoyed the community aspect, where I could connect with fellow learners.
Overall, this course was a great investment in my learning journey, and I’d highly recommend it to anyone interested in AI.
Abdulrahman Hassan

This course actually helped me to understand what artificial intelligence means in modern society and also I understand the concept used to develop AI to where we are today. It also serve as reference to me going forward concerning using AI to develop and accurately solve problems from whatever perspective in our lives. I also learn fundamental model that will help and guide in decision making . The only restriction I had in this course is limited examples and modern applications of most of instances they gave in the course if this lectures
HUSSEIN ALI ABDO ALI AL-WAJIH

In fact, these hours that I have spent in this course were more than wonderful. I have learned many valuable things from it, from reasoning and research to merging models and their divisions. I wished that this course would be longer - lasting, so I hope that you will fill us more with such valuable information and courses that help us to improve ourselves in this field
Manish Moses

Thank you helped to learn new skill hoping for the certificate learned many new things empowered my skills and used to implement in career or education sector just keep doing and improve the audio quality
Sakshi Rajput

Very good way of explaining and can help may people to learn and be future ready just a small suggestion to add questions for the viewers also so they can able to understand how much the under stand
APPU ANNAVEERAPPA

it was a great course and great learning from MIT lecturers from themselves.
hoping for more content like this and really excited to learn more about the artificial intelligence
NagaLakshmi Avula

Good course to learn new concepts in artificial intelligence ..but what about certification to those who learnt this course.??
Andre Spivey

Very informative and intuitive. The course actually goes into good depth concerning A.I. quizzes may be a good ideal to add to this course.
Jay Chauhan

This Course is very interesting and it covered all the things related to Artifical Inteligence, wonderful course
Littin Rajan

It was so cool. Learn most out of it. It tecaches most areas of Artificial Intelligence with real-life scenarios
Ajina Fathima

Its a really indepth knowledgeable class
You can add more quizz
You can add more interactive sections
Bhavesh Rajendra Sirole

It's very informative very helpful every single mamber can complete this coursing & earn the knowledge about artificial intelligence