Uniform cost search takes into account the cost associated with an action, and can be implemented with a priority queue.  Heuristics extimate the cost of the remaining path to the goal; the Manhattan distance is an example of an admissible heuristic.

Lecture 13: Optimizing a Search

Discussion of imperative, functional, and object-oriented programming styles.  State machines are introduced, using a turnstile as an example.

Lecture 2: Primitives, Combination, Abstraction, and Patterns 

Two search algorithms are introduced, depth-first search and breadth-first search.  Pruning rules (including dynamic programming) are also considered, so that the same state is not visited repeatedly.

Lecture 12: Search Algorithms

Introduction to probability theory, with the goals of making precise statements about uncertain situations and drawing reliable inferences from unreliable observations.  A hidden Markov model is then applied to robot navigation.

Lecture 10: Discrete Probability and State Estimation

Introduction to the four modules of 6.01 (software engineering, signals and systems, circuits, probability and planning).  The lecture then introduces object-oriented programming in Python, and ends with a discussion of environments.

Lecture 1: Object-Oriented Programming

Circuit design is complicated by interactions among elements, but these interactions can be reduced or eliminated by using an op-amp as a buffer.  This lecture covers how to analyze and design op-amp circuits.

Lecture 8: Op-Amps

System functions provide a summary of information that help optimize the design of a control system.  Poles are discussed further, based on their location on the unit circle.

Lecture 6: Designing Control Systems

This lecture covers other ways of achieving modularity in circuit design.  If a circuit only contains linear elements, then it can be represented by a Thevenin or Norton equivalent circuit, and superposition can be used.

Lecture 9: Circuit Abstractions

Introduction to poles, which provide a way to characterize the behavior of a system in terms of a mathematical description as a system function.

Lecture 5: Characterizing System Performance

Introduction to circuits, including several methods for analyzing circuits (Kirchoff's current and voltage laws, node voltages, and loop currents) and common patterns that simplify analysis.

Lecture 7: Circuits

OCW Scholar: Introduction to Electrical Engineering and Computer Science I

Introduction to signals and systems, focusing on multiple representations of discrete-time systems: difference equations, block diagrams, and operator representations.

Lecture 3: Signals and Systems

Recitation video covering dynamic programming, costs, and heuristics.

Recitation 16: Search: Dynamic Programming, Costs and Heuristics

Recitation video covering the basics of search.

Recitation 15: Search: Basics

Recitation video covering the basics of probability.

Recitation 13: Probability: Basics

Recitation video covering state estimation.

Recitation 14: Probability: State Estimation

Recitation 11: Op-Amps

Recitation video covering Thevenin/Norton equivalence and superposition.

Recitation 12: Thevenin/Norton Equivalence and Superposition

Recitation video covering the NVCC method of solving circuits, including a sample problem.

Recitation 10: Circuits: NVCC, Sample Problem

Recitation video covering circuit representations, Kirchhoff's voltage law, and Kirchhoff's current law.

Recitation 9: Circuits: Representation, KVL, KCL

Recitation video covering more information about poles, solving pole problems, and looking at unit sample responses and graphing poles on the unit circle.

Recitation 8: Poles, Part II

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