Mathematics
-
Minimum Spanning Trees (MSTs) via Kruskal and Prim
MSTs are the smallest connected subgraph that spans all the vertices of a given graph, minimizing the total edge weights.
-
Graph Algorithm Basics, BFS, and DFS
Graph: Graphs provide representations of relationships between entities, helping analyze and identify patterns in data. Learn more here!
-
Using Greedy and Dynamic Optimization Algorithms
Greedy algorithms take the best-looking choice at each step; dynamic programming solves overlapping subproblems once. Worked examples and when to use each.
-
Alternative Investment Funds: Algorithms as an Occupation
How occupations within alternative investment funds (AIFs) use algorithms, from quants and traders to network engineers.
-
Using Master Theorem Components and Formulations
The Master Theorem gives the time complexity of divide-and-conquer recurrences. Its components, three cases, and worked examples.
-
Recurrence Relations: Describing Efficiency and Optimization
Recurrence relations is a technique to analyze the time complexity of recursive algorithms. Recursion introduces trade-offs!
-
Recursion: Algorithmic Paradigms, Complexities, and Pitfalls
Recursion is where a function calls itself during its execution; its paradigm breaks a problem into smaller instances of the same problem.
-
Asymptotic Notation: Big O, Omega, and Theta
Asymptotic notation is the linguistic and mathematical bridge that allows developers to discuss the scalability and efficiency of algorithms.
-
Algorithm Development and Analysis
Algorithms are step-by-step procedures that solve problems. Learn how to judge an algorithm’s correctness and efficiency, with a binary search guessing game.