Videos

14. Learning: Sparse Spaces, Phonology

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston Why do “cats” and “dogs” end with different plural sounds, and how do we learn this? We can represent this problem in terms of distinctive features, and then generalize. We end this lecture with a brief discussion of how to approach […]

1. Introduction and Scope

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, Prof. Winston introduces artificial intelligence and provides a brief history of the field. The last ten minutes are devoted to information about the course at MIT. License: Creative Commons BY-NC-SA More information at ocw.mit.edu/terms More courses at ocw.mit.edu

Mega-R7. Near Misses, Arch Learning

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Mark Seifter This mega-recitation covers a question from the Fall 2007 final exam, in which we teach a robot how to identify a table lamp. Given a starting model, we identify a heuristic and adjust the model for each example; examples can be […]

2. Reasoning: Goal Trees and Problem Solving

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture covers a symbolic integration program from the early days of AI. We use safe and heuristic transformations to simplify the problem, and then consider broader questions of how much knowledge is involved, and how the knowledge is represented. License: […]

11. Learning: Identification Trees, Disorder

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we build an identification tree based on yes/no tests. We start by arranging the tree based on tests that result in homogeneous subsets. For larger datasets, this is generalized by measuring the disorder of subsets. License: Creative Commons […]

7. Constraints: Interpreting Line Drawings

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston How can we recognize the number of objects in a line drawing? We consider how Guzman, Huffman, and Waltz approached this problem. We then solve an example using a method based on constraint propagation, with a limited set of junction and […]

3. Reasoning: Goal Trees and Rule-Based Expert Systems

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston We consider a block-stacking program, which can answer questions about its own behavior, and then identify an animal given a list of its characteristics. Finally, we discuss how to extract knowledge from an expert, using the example of bagging groceries. License: […]

22. Probabilistic Inference II

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston We begin with a review of inference nets, then discuss how to use experimental data to develop a model, which can be used to perform simulations. If we have two competing models, we can use Bayes’ rule to determine which is […]

9. Constraints: Visual Object Recognition

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston We consider how object recognition has evolved over the past 30 years. In alignment theory, 2-D projections are used to determine whether an additional picture is of the same object. To recognize faces, we use intermediate-sized features and correlation. License: Creative […]

21. Probabilistic Inference I

4 years ago
* Please note: Lecture 20, which focuses on the AI business, is not available. MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston We begin this lecture with basic probability concepts, and then discuss belief nets, which capture causal relationships between events and allow us to specify the model more […]

15. Learning: Near Misses, Felicity Conditions

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston To determine whether three blocks form an arch, we use a model which evolves through examples and near misses; this is an example of one-shot learning. We also discuss other aspects of how students learn, and how to package your ideas […]

Mega-R3. Games, Minimax, Alpha-Beta

4 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Mark Seifter This mega-recitation covers Problem 1 from Quiz 2, Fall 2007. We start with a minimax search of the game tree, and then work an example using alpha-beta pruning. We also discuss static evaluation and progressive deepening (Problem 1-C, Fall 2008 Quiz […]