Related lectures (37)
Complexity Classes: P and NPMOOC: Information, Calcul, Communication: Introduction à la pensée informatique
Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.
Elements of computational complexity
Covers classical and quantum computational complexity concepts and implications.
Poles and Values: Simplification Techniques
Covers the correction of an intermediate test, suggestions for the exam, and simplification techniques of poles and values.
Search Algorithms: Dichotomy Search
Explores dichotomy search algorithms, analyzing complexity and implementation details for efficient searching in sorted lists.
Complexity of Algorithms
Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.
Design: Abstraction and Modularity
Emphasizes designing for actual needs, estimating complexity, and using design patterns like MVC, MVP, and MVVM.
Algorithms: Problem Solving and Graph Algorithms
Covers elementary graph algorithms, a midterm exam on algorithmic problem-solving, and distance measurement between strings.
Algorithm Analysis: Time ComplexityMOOC: Information, Calcul, Communication: Introduction à la pensée informatique
Covers algorithm temporal complexity evaluation and Big Theta notation for large input data.
Integer Factorization: Methods and Algorithms
Explores methods and algorithms for integer factorization, including testing for B smoothness and computing small primes.
Neural Networks: Single-Layer Control
Explores the implementation of single-layer neural network controllers and the impact of sensor numbers on behavior complexity.

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