Sessions

6. Search: Games, Minimax, and Alpha-Beta

6. Search: Games, Minimax, and Alpha-Beta

3 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 consider strategies for adversarial games such as chess. We discuss the minimax algorithm, and how alpha-beta pruning improves its efficiency. We then examine progressive deepening, which ensures that some answer is always available. License: Creative Commons BY-NC-SA […]
16. Learning: Support Vector Machines

16. Learning: Support Vector Machines

3 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 explore support vector machines in some mathematical detail. We use Lagrange multipliers to maximize the width of the street given certain constraints. If needed, we transform vectors into another space, using a kernel function. License: Creative Commons […]
Mega-R4. Neural Nets

Mega-R4. Neural Nets

3 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Mark Seifter We begin by discussing neural net formulas, including the sigmoid and performance functions and their derivatives. We then work Problem 2 of Quiz 3, Fall 2008, which includes running one step of back propagation and matching neural nets with classifiers. License: […]
Mega-R6. Boosting

Mega-R6. Boosting

3 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 the boosting problem from Quiz 4, Fall 2009. We determine which classifiers to use, then perform three rounds of boosting, adjusting the weights in each round. This gives us an expression for the final classifier. License: Creative Commons […]
Mega-R1. Rule-Based Systems

Mega-R1. Rule-Based Systems

3 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Mark Seifter In this mega-recitation, we cover Problem 1 from Quiz 1, Fall 2009. We begin with the rules and assertions, then spend most of our time on backward chaining and drawing the goal tree for Part A. We end with a brief […]
5. Search: Optimal, Branch and Bound, A*

5. Search: Optimal, Branch and Bound, A*

3 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston This lecture covers strategies for finding the shortest path. We discuss branch and bound, which can be refined by using an extended list or an admissible heuristic, or both (known as A*). We end with an example where the heuristic must […]
17. Learning: Boosting

17. Learning: Boosting

3 years ago
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: ocw.mit.edu/6-034F10 Instructor: Patrick Winston Can multiple weak classifiers be used to make a strong one? We examine the boosting algorithm, which adjusts the weight of each classifier, and work through the math. We end with how boosting doesn’t seem to overfit, and mention some applications. […]
Mega-R3. Games, Minimax, Alpha-Beta

Mega-R3. Games, Minimax, Alpha-Beta

3 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 […]
15. Learning: Near Misses, Felicity Conditions

15. Learning: Near Misses, Felicity Conditions

3 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 […]
21. Probabilistic Inference I

21. Probabilistic Inference I

3 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 […]
9. Constraints: Visual Object Recognition

9. Constraints: Visual Object Recognition

3 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 […]
22. Probabilistic Inference II

22. Probabilistic Inference II

3 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 […]