Lectures related to Introduction to Algorithms

Covers the basics of image processing, focusing on writing a program to process images.

C++ Standard Library: Containers and Algorithms

Covers the basics of C++ standard library containers, algorithms, and iterators.

Introduction to Algorithms: Course Overview and Basics

Introduces the CS-250 Algorithms course, covering its structure, objectives, and key topics in algorithmic problem-solving.

Programming Concepts: Variables and Expressions

Covers fundamental programming concepts such as algorithms, variables, and expressions in C++.

Introduction to Compiler Theory and Language Processing

Introduces compiler theory, language processing, and the essential concepts behind building compilers.

Programming for Engineers: Advanced MATLAB Techniques

Explores advanced MATLAB techniques, emphasizing vectorization, 'find' function, and plot manipulation.

Quasi-newton optimization

Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.

Introduction to Computational Thinking

Introduces computational thinking, empowering individuals to create solutions through technology.

Optimization algorithms

Covers optimization algorithms, focusing on Proximal Gradient Descent and its variations.

Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Multi-arm Bandits

Discusses algorithms for balancing exploration and exploitation in decision-making processes.

Variance Reduction: Strategies and Applications

Discusses variance reduction techniques in stochastic simulation, focusing on allocation strategies and replica generation algorithms.

Introduction to Computational Thinking

Explores the importance of computational thinking in modern society and provides hands-on programming experience.

Introduction to Programming: First Steps

Covers the basics of programming, including the development cycle of a program, strong typing, data storage in Java variables, and primitive data types.

Introduction to Programming: Basics of Python

Covers the basics of programming in Python, focusing on practical skills and hands-on exercises.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Set Cover: Integrality Gap

Explores the integrality gap concept in set cover and multiplicative weights algorithms.

Constraint Satisfaction: Formulation and Algorithms

Covers the formulation of constraint satisfaction problems and systematic algorithms for solving them efficiently.

Programming basics: introduction and algorithms

Introduces programming basics, covering Unix systems, Firefox, algorithms, and data structures.