Lectures related to Algorithms: Summary of the week

Provides an overview of essential search and sort algorithms in computer science.

Explores optimization problems and greedy algorithms for efficient decision-making.

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

Explores binary search, merge sort, recursion in algorithms, Fibonacci numbers, and dynamic programming.

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

Introduces recursion and binary search algorithms for efficient problem-solving.

Explores worst-case complexity, mathematical induction, and algorithms like binary search and insertion sort.

Covers the complexity analyses of search and sorting algorithms.

Explains the merge sort algorithm, its correctness, and time complexity compared to other sorting algorithms.

Explores linear search, insertion sort, coin change, maximum matching, and the halting problem in algorithms.

Covers the complexity analyses of algorithms and their worst-case time complexities.

Covers the basics of algorithms, the importance of studying them, data structures, and the impact of algorithms on various fields.

Explains binary search, the logarithm function, and recursive algorithms for factorial and Fibonacci sequences.

Explores dynamic programming for palindromic subsequences, merging binary search trees, and finding the median of two sorted arrays.

Explains the binary search algorithm using two pointers to efficiently search a sorted list.

Covers recursion, dynamic programming, and algorithm design using divide and conquer strategies.

Covers hashing, sorting, extendible hashing, linear hashing, and external sorting.

Covers the binary search algorithm and provides a step-by-step example.

Explores the binary search algorithm's efficiency in reducing search time.

Introduces selection and insertion sorting algorithms, explaining their correctness and time complexity.