Introduction to Algorithms
by MIT
This course material is only available in the iTunes U app on iPhone or iPad.
Course Description
| Name | Description | Time | Price | ||
|---|---|---|---|---|---|
| 1 | Lecture Notes 1: Analysis of Algorithms | -- | -- | Free | View in iTunes |
| 2 | Lecture Notes 2: Asymptotic Notation and Recurrences | -- | -- | Free | View in iTunes |
| 3 | Lecture Notes 3: Divide and Conquer | -- | -- | Free | View in iTunes |
| 4 | Lecture Notes 4: Quicksort | -- | -- | Free | View in iTunes |
| 5 | Lecture Notes 5: Linear-time Sorting: Lower Bounds, Counting Sort, Radix Sort | -- | -- | Free | View in iTunes |
| 6 | Lecture Notes 6: Order Statistics, Median | -- | -- | Free | View in iTunes |
| 7 | Lecture Notes 7: Hashing, Hash Functions | -- | -- | Free | View in iTunes |
| 8 | Lecture Notes 8: Universal Hashing, Perfect Hashing | -- | -- | Free | View in iTunes |
| 9 | Lecture Notes 9: Relation of BSTs to Quicksort - Analysis of Random BST | -- | -- | Free | View in iTunes |
| 10 | Lecture Notes 10: Red-black Trees, Rotations, Insertions, Deletions | -- | -- | Free | View in iTunes |
| 11 | Lecture Notes 11: Augmenting Data Structures, Dynamic Order Statistics, Interval Trees | -- | -- | Free | View in iTunes |
| 12 | Lecture Notes 12: Skip Lists | -- | -- | Free | View in iTunes |
| 13 | Lecture Notes 13: Amortized Algorithms, Table Doubling, Potential Method | -- | -- | Free | View in iTunes |
| 14 | Lecture Notes 14: Competitive Analysis: Self-organizing Lists | -- | -- | Free | View in iTunes |
| 15 | Lecture Notes 15: Dynamic Programming, Longest Common Subsequence | -- | -- | Free | View in iTunes |
| 16 | Lecture Notes 16: Greedy Algorithms, Minimum Spanning Trees | -- | -- | Free | View in iTunes |
| 17 | Lecture Notes 17: Shortest Paths I: Properties, Dijkstra's Algorithm, Breadth-first Search | -- | -- | Free | View in iTunes |
| 18 | Lecture Notes 18: Shortest Paths II: Bellman-Ford, Linear Programming, Difference Constraints | -- | -- | Free | View in iTunes |
| 19 | Lecture Notes 19: Shortest Paths III: All-pairs Shortest Paths, Matrix Multiplication, Floyd-Warshall, Johnson | -- | -- | Free | View in iTunes |
| 20 | Lecture Notes: Skip Lists | -- | -- | Free | View in iTunes |
| 21 | Problem Set 1 | -- | -- | Free | View in iTunes |
| 22 | Problem Set 1 Solutions | -- | -- | Free | View in iTunes |
| 23 | Problem Set 2 | -- | -- | Free | View in iTunes |
| 24 | Problem Set 2 Solutions | -- | -- | Free | View in iTunes |
| 25 | Problem Set 3 | -- | -- | Free | View in iTunes |
| 26 | Problem Set 3 Solutions | -- | -- | Free | View in iTunes |
| 27 | Problem Set 4 | -- | -- | Free | View in iTunes |
| 28 | Problem Set 4 Solutions | -- | -- | Free | View in iTunes |
| 29 | Problem Set 5 | -- | -- | Free | View in iTunes |
| 30 | Problem Set 5 Solutions | -- | -- | Free | View in iTunes |
| 31 | Problem Set 6 | -- | -- | Free | View in iTunes |
| 32 | Problem Set 6 Solutions | -- | -- | Free | View in iTunes |
| 33 | Problem Set 7 | -- | -- | Free | View in iTunes |
| 34 | Problem Set 7 Solutions | -- | -- | Free | View in iTunes |
| 35 | Problem Set 7: sample | -- | -- | Free | View in iTunes |
| 36 | Problem Set 7: samplesol | -- | -- | Free | View in iTunes |
| 37 | Problem Set 7: input1 | -- | -- | Free | View in iTunes |
| 38 | Problem Set 7: input2 | -- | -- | Free | View in iTunes |
| 39 | Problem Set 7: input3 | -- | -- | Free | View in iTunes |
| 40 | Problem Set 8 | -- | -- | Free | View in iTunes |
| 41 | Problem Set 8 Solutions | -- | -- | Free | View in iTunes |
| 42 | Problem Set 9 | -- | -- | Free | View in iTunes |
| 43 | Problem Set 9 Solutions | -- | -- | Free | View in iTunes |
| 44 | Practice Quiz 1 | -- | -- | Free | View in iTunes |
| 45 | Practice Quiz 1 Solutions | -- | -- | Free | View in iTunes |
| 46 | Quiz 1 | -- | -- | Free | View in iTunes |
| 47 | Quiz 1 Solutions | -- | -- | Free | View in iTunes |
| 48 | Quiz 2 Practice | -- | -- | Free | View in iTunes |
| 49 | Quiz 2 | -- | -- | Free | View in iTunes |
| 50 | Quiz 2 Solutions | -- | -- | Free | View in iTunes |
| 51 | Practice Final | -- | -- | Free | View in iTunes |
| 52 | Practice Final Exam Solutions | -- | -- | Free | View in iTunes |
| 53 | Final Exam | -- | -- | Free | View in iTunes |
| 54 | Final Exam Solutions | -- | -- | Free | View in iTunes |
| 55 | Dynamic Algorithms Handout | -- | -- | Free | View in iTunes |
| 56 | Skip Lists Handout | -- | -- | Free | View in iTunes |
| 56 Items |
Customer Reviews
Bad video quality
I usually treat these video like actual course, so I take note and do exercises after finishing a lecture. However, due to the extremely low quality of the video it's quite difficult take some good notes. I need to enlarge and shrink the screen to see what the professor wrote. Also I only enjoyed the sorting algorithms but not too concern about the derivation (since that's all i need to digest in order to implement certain methods in my code anyway). The derivation part is just way too tedious. In fact sometime I wasn't sure if the professor knows what he's talking about.
Great content
The content of is course is very good but the video could def use some polishing (Would HD be too much to ask?). Aside from the poor video(I ended up listening to the audio and reading the slides instead of watching it), i was genuinely impressed. I am currently a Computer Science PhD candidate and I used the material to augment my algorithms course this semester (mostly to review prerequisite topics). I thought the professors laid out the topic very well and covered everything very well. I definitely recommend it to anyone that is studying BS or MS level algorithms.
This is the holy grail of Algorithms
If you have any interest in learning Algorithms the right way this is one of the best resources on the planet.
Every word spoken by Eric Demaine has so much information, and hidden gems its unbelievable.
You can read many algorithm books, take note of the end result of the running time analysis of the various algorithms, and the efficiency of the data structures. However unless you understand how to derive these performance metrics you will not be able to develop your own versions of these algorithms and be confident about the performance.
My suggestion to ppl interested would be to do this course as if they were at MIT. Watch 2-3 lectures a week, take notes and DO THE PROBLEM SETS! I find that most of the problem sets have helped me right better code for the analytics team at my full time job!
