Lectures related to Algorithms: Efficiency Analysis

Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.

Introduces greedy algorithms and matroids, highlighting their efficiency in solving optimization problems.

Explores critical phenomena in complex systems, including stochastic objects, percolation, and combinatorial optimization.

Modular Arithmetic: Exponentiation Optimization

Explores optimizing exponentiation in modular arithmetic for efficient calculations and prime number determination.

Elements of computational complexity

Covers classical and quantum computational complexity concepts and implications.

Dynamic Programming: Knapsack

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

Dynamic Programming: Solving Sequential Problems Efficiently

Explores dynamic programming for efficient problem-solving, illustrated with binomial coefficients and Pascal's triangle.

Algorithmic Challenges: Solutions and Optimization

Explores algorithmic challenges, time complexity, optimization, recursion, and probability calculations.

Distributed Information Retrieval

Explores centralized and distributed information retrieval, including Fagin's Algorithm for efficient document identification.

Algorithmic Complexity: Travel Time Analysis

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.

Algorithmic Complexity: Theta Notation

Explores algorithmic complexity, comparing growth rates using Theta notation and characterizing different complexity classes.

Algorithmic Complexity: Visualization and Analysis

Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.

Solving Parity Games in Practice

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Complexity & Induction: Algorithms & Proofs

Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.

Binary Search: Basics and Execution

Explores binary search fundamentals, efficiency, and temporal complexity in algorithmic searching.

Complexity of Algorithms

Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.

Linear Algebra: Efficiency and Complexity

Explores constraints, efficiency, and complexity in linear algebra, emphasizing convexity and worst-case complexity in algorithm analysis.

Subquadratic Attention Mechanisms: State Space Models Overview

Covers subquadratic attention mechanisms and state space models, focusing on their theoretical foundations and practical implementations in machine learning.

Knapsack Problem: Optimization and Traveling Salesman

Explores the knapsack problem and the traveling salesman problem with a focus on optimization algorithms.

Complexity of Algorithms: Proofs of Time Complexity

Covers the analysis of worst time complexity for algorithms and time complexity with real numbers and integers.