31 episodios

In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabilistic inference. In these mega-recitations, teaching assistant Mark Seifter works through problems from previous exams in a lecture-style setting. Students are asked to participate, and emphasis is placed on being able to work the algorithms by hand.

Artificial Intelligence MIT

    • Tecnología

In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabilistic inference. In these mega-recitations, teaching assistant Mark Seifter works through problems from previous exams in a lecture-style setting. Students are asked to participate, and emphasis is placed on being able to work the algorithms by hand.

    • video
    Lecture 1: Introduction and Scope

    Lecture 1: Introduction and Scope

    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.

    • 47 min
    • video
    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 to simplify the problem, and then consider broader questions of how much knowledge is involved, and how the knowledge is represented.

    • 45 min
    • video
    Lecture 3: Reasoning: Goal Trees and Rule-Based Expert Systems

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

    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.

    • 49 min
    • video
    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 of nodes already considered, hill climbing, and beam search. We end with a brief discussion of commonsense vs. reflective knowledge.

    • 48 min
    • video
    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 using an extended list or an admissible heuristic, or both (known as A*). We end with an example where the heuristic must be consistent.

    • 48 min
    • video
    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 alpha-beta pruning improves its efficiency. We then examine progressive deepening, which ensures that some answer is always available.

    • 48 min

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