MIT

7. Principle of equivalence continued; parallel transport. (M-I-T)

Lecture 18: Representations: Classes, Trajectories, Transitions (M-I-T)

Lecture 24: Advanced Topics (cont.) (M-I-T)

8. Lie transport, Killing vectors, tensor densities. (M-I-T)

Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind (M-I-T)

Lecture 25: Advanced Topics (cont.) – Discussion of Follow-on Classes (M-I-T)

9. Geodesics. (M-I-T)

Lecture 1: Introduction and Scope (M-I-T)

Lecture 2: Asymptotic Notation; Recurrences; Substitution, Master Method (M-I-T)

Lecture 21: Probabilistic Inference I (M-I-T)

Lecture 3: Divide-and-Conquer: Strassen, Fibonacci, Polynomial Multiplication (M-I-T)

Lecture 22: Probabilistic Inference II (M-I-T)

Lecture 4: Quicksort, Randomized Algorithms (M-I-T)

Lecture 23: Model Merging, Cross-Modal Coupling, Course Summary (M-I-T)

Lecture 5: Linear-time Sorting: Lower Bounds, Counting Sort, Radix Sort (M-I-T)

Lecture 2: Reasoning: Goal Trees and Problem Solving (M-I-T)

Lecture 6: Order Statistics, Median (M-I-T)

Lecture 3: Reasoning: Goal Trees and Rule-Based Expert Systems (M-I-T)

Lecture 7: Hashing, Hash Functions (M-I-T)

Lecture 4: Search: Depth-First, Hill Climbing, Beam (M-I-T)

Lecture 8: Universal Hashing, Perfect Hashing (M-I-T)

Lecture 5: Search: Optimal, Branch and Bound, A* (M-I-T)

Lecture 9: Relation of BSTs to Quicksort – Analysis of Random BST (M-I-T)

Lecture 6: Search: Games, Minimax, and Alpha-Beta (M-I-T)

Lecture 7: Constraints: Interpreting Line Drawings (M-I-T)