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Related lectures (22)
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Linear Programming: Optimization and Constraints
Explores linear programming optimization with constraints, Dijkstra's algorithm, and LP formulations for finding feasible solutions.
Algorithmic Complexity: Visualization and Analysis
Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.
Complexity & Induction: Algorithms & Proofs
Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.
Algorithmic Complexity: Travel Time Analysis
Covers control operations, algorithmic complexity, function calls, and travel time analysis.
Group Membership and View Synchrony
Explores group membership and view synchrony in distributed systems, ensuring accurate knowledge and coordinated views.
Testing of VLSI Systems
Explores defects, errors, and fault models in VLSI systems, emphasizing fault collapsing and dominance.
Calcul and Algorithms: What is an Algorithm?
Covers the basics of algorithms in computer science, including their formalization and complexity.
Basic Ingredients of Algorithms
Covers the basic elements of algorithms, including data handling, instructions, and control structures.
Support Vector Machines: Formulation and Complexity
Explores the formulation and complexity of Support Vector Machines, including primal and dual forms, geometric interpretation, and algorithmic implications.
Dynamic Programming: Shortest Paths Algorithms
Explores dynamic programming strategies for finding shortest paths in networks with various algorithms and complexities.
