Skip to content

MIT 6.006 — Introduction to Algorithms

Introduction

MIT 6.006 is MIT's introductory algorithms course, taught by Professors Erik Demaine, Srini Devadas, and Nancy Lynch. The course systematically covers fundamental methods of algorithm design and analysis, including sorting, searching, graph algorithms, dynamic programming, and greedy algorithms.

Unlike CS61B which emphasizes hands-on implementation, MIT 6.006 focuses more on understanding and analysis. The course emphasizes mathematical proofs and complexity analysis — you need to not only know how to use an algorithm, but also prove why it's correct and analyze its time complexity. This theoretical training is essential for deeply understanding the nature of algorithms.

The course uses Python as the teaching language, lowering the syntax barrier so you can focus on the algorithms themselves.

Prerequisites

  • Programming fundamentals: Familiarity with Python or at least one programming language
  • Basic mathematics: High school math level, comfortable with logarithms, exponents, summations
  • Discrete math (recommended): Basic set theory, logic, mathematical induction

Difficulty

🌟🌟⭐ (Medium-Upper)

The theoretical portions (complexity proofs, correctness proofs for dynamic programming) can be challenging for students without a strong math background. However, the lectures are very clearly explained, and with videos and notes you can keep up.

Estimated Study Hours

Approximately 80-100 hours

  • Lectures: ~30 hours
  • Problem sets: ~30-40 hours
  • Review and exam prep: ~20-30 hours

Course Resources

  • Course website: MIT OCW 6.006
  • Lecture videos: MIT OCW YouTube
  • Lecture notes and assignments: Complete lecture notes and problem sets available on the course website
  • Textbook: Introduction to Algorithms (CLRS) as reference

Self-Study Tips

  1. Do the problem sets. MIT's problem sets are exceptionally well-designed. Watching videos without doing problems means you haven't really learned.

  2. Take complexity analysis seriously. This is the core skill of MIT 6.006. For every algorithm you learn, be able to state and prove its time and space complexity.

  3. Dynamic programming is the watershed. Many students get stuck here. Start by understanding "optimal substructure" and "overlapping subproblems," then practice with classic DP problems (knapsack, longest common subsequence, matrix chain multiplication).

  4. Pair with LeetCode practice. After finishing a topic, find corresponding problems on LeetCode. The theory + practice combination works best.

  5. If CLRS is too hard, read Algorithms, 4th Edition first. Sedgewick's book is more intuitive and works great as supplementary reading for MIT 6.006.

  6. Use the MIT OCW recitation videos. These problem-solving sessions are extremely helpful for understanding the problem sets.