Discover MIT's free online course, Introduction to Algorithms, covering data structures, dynamic programming, sorting, searching techniques, and much more in IT.
Welcome to "Introduction to Algorithms," a sought-after course with an outstanding 5-star rating. This comprehensive course, aimed at providing an in-depth understanding of algorithms, spans 35 hours and 19 minutes and falls under the Information Technology category, specifically focusing on Programming Logic. With an impressive array of topics and practical problem sessions, this course delivers a robust foundation in algorithms and computation.
Starting with the basics, the course introduces you to fundamental concepts in algorithms and their computational implications. The journey begins with an exploration of data structures and dynamic arrays, equipping you with the essential tools to understand higher-level algorithmic strategies.
In the early stages, you will delve into the asymptotic behavior of functions, learning to analyze and compare the efficiency of various algorithms. The course includes a practical problem session to reinforce your understanding of these concepts through double-ended problem-solving techniques.
As you progress, you will explore sets and sorting algorithms, gaining insights into how to efficiently organize and retrieve data. The topic of hashing will also be addressed, offering you a deep dive into one of the fastest data lookup methods available.
The course doesn't shy away from complex topics either. You'll engage with linear sorting algorithms and their applications, understanding their advantages and limitations. The concept of binary trees is thoroughly examined over two comprehensive parts, including a focused session on AVL trees, a type of self-balancing binary search tree.
Further along, the curriculum covers binary heaps and explores breadth-first search (BFS), a key algorithm for traversing and searching tree or graph data structures. Depth-first search (DFS) is also covered, offering another critical perspective on algorithmic problem-solving.
Weighted shortest paths, such as those solved by the Bellman-Ford and Dijkstra algorithms, are a highlight of the course. These topics are vital for anyone looking to understand pathfinding in weighted graphs. The All-Pairs Shortest Path (APSP) algorithm and Johnson's algorithm for sparse graphs are also discussed.
Dynamic programming, a method for solving complex problems by breaking them down into simpler subproblems, is covered extensively. You will learn and apply dynamic programming techniques across a variety of problems, from sequence alignment to coin change and longest common subsequence (LCS).
The course also touches on the principles of algorithmic complexity, preparing you for more advanced topics and helping you understand the boundaries and possibilities within the field of algorithmic problem-solving.
The grand finale involves a comprehensive course review and insights into the next steps in your algorithmic education. Whether you're a novice looking to get a solid grounding in algorithms or a seasoned programmer aiming to refine and expand your skills, this course offers unparalleled depth and quality.
Video class: 1. Algorithms and Computation
0h45m
Exercise: _What is the usual way of defining a computational problem?
Video class: 2. Data Structures and Dynamic Arrays
0h50m
Video class: Introduction to Algorithms - Problem Session 1: Asymptotic Behavior of Functions and Double-ended...
1h26m
Exercise: _What is the purpose of the 6.006 problem sessions?
Exercise: _Which of the following functions grows asymptotically slower than any polynomial function?
Video class: 3. Sets and Sorting
0h52m
Video class: 4. Hashing
0h52m
Exercise: _What is the set interface used for in the context of the lecture?
Exercise: _What is the model of computation used in this lecture to prove the lower bound on search algorithms?
Video class: Problem Session 2 (MIT 6.006 Introduction to Algorithms, Spring 2020)
1h27m
Video class: 5. Linear Sorting
0h51m
Exercise: _What is the problem with the direct access array?
Exercise: _What is the advantage of using a direct access array?
Video class: Problem Session 3
1h26m
Video class: 6. Binary Trees, Part 1
0h50m
Exercise: _What is the data structure that is almost superior to all data structures mentioned in the lecture?
Exercise: _What is the data structure that is almost superior to all other data structures and can do almost anything really fast?
Video class: 7. Binary Trees, Part 2: AVL
0h54m
Video class: Problem Session 4
1h29m
Exercise: _What data structure performs well on dictionary operations but poorly on order operations?
Video class: 8. Binary Heaps
0h50m
Video class: 9. Breadth-First Search
0h52m
Exercise: _What is the difference between the set notation and the notation on the screen/board in the context of undirected and directed graphs?
Video class: Quiz 1 review
1h25m
Video class: 10. Depth-First Search
0h52m
Exercise: _What is the definition of a graph?
Exercise: _What are the two algorithms that are almost always seen together in graph theory?
Video class: 11. Weighted Shortest Paths
0h57m
Video class: Problem Session 5
1h28m
Exercise: _What is the definition of the radius of a graph in the context of the problem discussed in the lecture?
Exercise: _What is the eccentricity of a point in a circle?
Video class: 12. Bellman-Ford
0h57m
Video class: Problem Session 6
1h26m
Exercise: _What is the purpose of Dijkstra's algorithm?
Exercise: _What is the purpose of Dijkstra's algorithm?
Video class: 13. Dijkstra
0h57m
35 hours and 19 minutes of online video course
Exercises to train your knowledge
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Course comments: Introduction to algorithms
Doulov Ulug?bek Hayitbayevich
very good ????