Artificial Intelligence
- Offered byMIT Professional Education
Artificial Intelligence at MIT Professional Education Overview
Duration | 12 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Artificial Intelligence at MIT Professional Education Highlights
- Earn a Certificate of completion from MIT on successful course completion
- Instructor - Prof. Patrick Henry Winston
- The course offers an introduction to basic knowledge representation, problem solving, and learning methods of artificial intelligence
Artificial Intelligence at MIT Professional Education Course details
- This course is designed for those who want to learn skills fundamental to artificial intelligence.
- 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.
- 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.
Artificial Intelligence at MIT Professional Education Curriculum
Lecture 1: Introduction and Scope
Lecture 2: Reasoning: Goal Trees and Problem Solving
Lecture 3: Reasoning: Goal Trees and Rule-Based Expert Systems
Lecture 4: Search: Depth-First, Hill Climbing, Beam
Lecture 5: Search: Optimal, Branch and Bound, A*
Lecture 6: Search: Games, Minimax, and Alpha-Beta
Lecture 7: Constraints: Interpreting Line Drawings
Lecture 8: Constraints: Search, Domain Reduction
Lecture 9: Constraints: Visual Object Recognition
Lecture 10: Introduction to Learning, Nearest Neighbors
Lecture 11: Learning: Identification Trees, Disorder
Lecture 12A: Neural Nets
Lecture 12B: Deep Neural Nets
Lecture 13: Learning: Genetic Algorithms
Lecture 14: Learning: Sparse Spaces, Phonology
Lecture 15: Learning: Near Misses, Felicity Conditions
Lecture 16: Learning: Support Vector Machines
Lecture 17: Learning: Boosting
Lecture 18: Representations: Classes, Trajectories, Transitions
Lecture 18: Representations: Classes, Trajectories, Transitions
Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind
Lecture 20: The AI business
Lecture 21: Probabilistic Inference I
Lecture 22: Probabilistic Inference II
Lecture 23: Model Merging, Cross-Modal Coupling, Course Summary
Other courses offered by MIT Professional Education
Student Forum
Anything you would want to ask experts?