Ranked #1
Lecture 2: Reasoning: Goal Trees and Problem Solving
Lecture 2: Reasoning: Goal Trees and Problem Solving
This lecture covers a symbolic integration program from the early days of AI. We use safe and heuristic transformations... Read more
25 Nov 2013
•
45mins
Ranked #2
Lecture 4: Search: Depth-First, Hill Climbing, Beam
Lecture 4: Search: Depth-First, Hill Climbing, Beam
This lecture covers algorithms for depth-first and breadth-first search, followed by several refinements: keeping track ... Read more
25 Nov 2013
•
48mins
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Ranked #3
Lecture 12: Learning: Neural Nets, Back Propagation
Lecture 12: Learning: Neural Nets, Back Propagation
How do we model neurons? In the neural net problem, we want a set of weights that makes the actual output match the des... Read more
25 Nov 2013
•
47mins
Ranked #4
Mega-Recitation 1: Rule-Based Systems
Mega-Recitation 1: Rule-Based Systems
In this mega-recitation, we cover Problem 1 from Quiz 1, Fall 2009. We begin with the rules and assertions, then spend ... Read more
25 Nov 2013
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46mins
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Ranked #5
Lecture 13: Learning: Genetic Algorithms
Lecture 13: Learning: Genetic Algorithms
This lecture explores genetic algorithms at a conceptual level. We consider three approaches to how a population evolve... Read more
25 Nov 2013
•
47mins
Ranked #6
Lecture 5: Search: Optimal, Branch and Bound, A*
Lecture 5: Search: Optimal, Branch and Bound, A*
This lecture covers strategies for finding the shortest path. We discuss branch and bound, which can be refined by usin... Read more
25 Nov 2013
•
48mins
Ranked #7
Lecture 10: Introduction to Learning, Nearest Neighbors
Lecture 10: Introduction to Learning, Nearest Neighbors
This lecture begins with a high-level view of learning, then covers nearest neighbors using several graphical examples. ... Read more
25 Nov 2013
•
49mins
Ranked #8
Lecture 6: Search: Games, Minimax, and Alpha-Beta
Lecture 6: Search: Games, Minimax, and Alpha-Beta
In this lecture, we consider strategies for adversarial games such as chess. We discuss the minimax algorithm, and how ... Read more
25 Nov 2013
•
48mins
Ranked #9
Lecture 17: Learning: Boosting
Lecture 17: Learning: Boosting
Can multiple weak classifiers be used to make a strong one? We examine the boosting algorithm, which adjusts the weight... Read more
25 Nov 2013
•
51mins
Ranked #10
Lecture 15: Learning: Near Misses, Felicity Conditions
Lecture 15: Learning: Near Misses, Felicity Conditions
To determine whether three blocks form an arch, we use a model which evolves through examples and near misses; this is a... Read more
25 Nov 2013
•
46mins
Ranked #11
Lecture 21: Probabilistic Inference I
Lecture 21: Probabilistic Inference I
We begin this lecture with basic probability concepts, and then discuss belief nets, which capture causal relationships ... Read more
25 Nov 2013
•
48mins
Ranked #12
Mega-Recitation 4: Neural Nets
Mega-Recitation 4: Neural Nets
We begin by discussing neural net formulas, including the sigmoid and performance functions and their derivatives. We t... Read more
25 Nov 2013
•
52mins
Ranked #13
Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind
Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind
In this lecture, we consider cognitive architectures, including General Problem Solver, SOAR, Emotion Machine, Subsumpti... Read more
25 Nov 2013
•
49mins
Ranked #14
Lecture 22: Probabilistic Inference II
Lecture 22: Probabilistic Inference II
We begin with a review of inference nets, then discuss how to use experimental data to develop a model, which can be use... Read more
25 Nov 2013
•
48mins
Ranked #15
Lecture 14: Learning: Sparse Spaces, Phonology
Lecture 14: Learning: Sparse Spaces, Phonology
Why do "cats" and "dogs" end with different plural sounds, and how do we learn this? We can represent this problem in t... Read more
25 Nov 2013
•
47mins
