Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Algorithms: Design and Analysis, Part 2 online course by edX, is aimed at learners who have at least some experience in programming. It is a self-paced intermediate-level programme with a rigorous curriculum that focuses more on conceptual understanding and the bigger picture than mathematical details and low-level implementation.
The topics covered under the Algorithms: Design and Analysis, Part 2 certification syllabus include Greedy Algorithms (Clustering, Scheduling, Huffman Codes, Minimum Spanning Trees), Dynamic Programming (Sequence Alignment, Shortest Paths, Optimal Search Trees, Knapsack), NP-Completeness, Local Search, and Analysis of Heuristics.
The Algorithms: Design and Analysis, Part 2 training programme by edX includes various assessments through which learners can practice and master Algorithms. These include six multiple-choice problem sets, six programming assignments, and a final multiple-choice exam.
Moreover, there is no due date for these assignments, allowing learners to work through the course at their own pace. But to complete the course comfortably within six weeks, they must study two to four hours a week. Edx has 2 tracks namely the verified track and the audit track which allows the students to finish off the course. During the verified track, the candidates will get unlimited but paid access to the course materials. In the audit track, the candidates will be getting limited but free access to the course materials.
The Highlights
- Six-Week Course
- Learning Free of Cost
- Learn at Your Own Pace
- Intermediate Level Course
- Video Transcripts in English
- Two to Four Hours Per Week
- Six Programming Assignments
- Stanford University Programme
- Six Multiple-Choice Problem Sets
- Shareable Completion Certificate
Programme Offerings
- Learn for Free
- Six-week Course
- Learn at your own pace
- intermediate level course
- Six Programming Assignments
- Shareable Completion Certificate
- Two to Four Hours Per Week
- Stanford University programme
- Video transcripts in English
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 12404 | yes | Stanford |
- Interested candidates can audit the Algorithms: Design and Analysis, Part 2 certification fee for free.
- Only candidates who have purchased a "Verified Certificate" experience will receive the course certificate.
Algorithms: Design and Analysis, Part 2 fee structure
Training | Fee in INR |
Algorithms: Design and Analysis, Part 2 (Audit only) | NA. |
Algorithms: Design and Analysis, Part 2 (With certificate) | Rs. 12,404 |
Eligibility Criteria
Interested candidates should have at least some experience in programming. Because the topics covered under the Algorithms: Design and Analysis, Part 2 certification by edX, are typically taught in the third year of a University Computer Science programme.
What you will learn
After completing the Algorithms: Design and Analysis, Part 2 certification syllabu, learners should be well-versed in:
- Dynamic Programming (Sequence Alignment, Shortest Paths, Optimal Search Trees, Knapsack)
- Greedy Algorithms (Clustering, Scheduling, Huffman Codes, Minimum Spanning Trees)
- Analysis of Heuristics
- NP-Completeness
- Local Search
Who it is for
Though this Algorithms: Design and Analysis, Part 2 course suits all still the ideal audience would be the ones who want to become computer programmers.
Admission Details
Step 1: Land on the Algorithms: Design and Analysis, Part 2 classes webpage by clicking on the provided link here:
https://www.edx.org/course/algorithms-design-and-analysis-part-2-2
Step 2: Select the "Enroll" button present at the top corner of the webpage.
Step 3: Sign in to your edX account by entering your credentials. Create a new edX account if you do not have one. Otherwise, register with either your Facebook, Google, Microsoft, or Apple account.
Step 4: Next, choose a suitable enrolment option, pay the necessary fee, and complete the procedure.
Application Details
Applying for Algorithms: Design and Analysis, Part 2 certification course only requires submitting an online registration form. Candidates need not fill any mandatory forms.
The Syllabus
- Overview, Resources, and Policies
- Pre-Course Survey
- All Lecture slides
- Overview
- Application: Internet Routing (10 min)
- Application: Sequence Alignment (8 min)
- Introduction to Greedy Algorithms (12 min)
- Application: Optimal Caching (10 min)
- Problem Definition (5 min)
- A Greedy Algorithm (12 min)
- Correctness Proof - Part I (6 min)
- Correctness Proof - Part II (4 min)
- Handling Ties [Advanced - Optional] (7 min)
- MST Problem Definition (11 min)
- Prim's MST Algorithm (7 min)
- Correctness Proof I (15 min)
- Correctness Proof II (8 min)
- Proof of Cut Property [Advanced - Optional] (11 min)
- Fast Implementation I (14 min)
- Fast Implementation II (9 min)
- Problem Set 1
- Optional Theory Problems
- Programming Assignment 1
- Overview
- Kruskal's MST Algorithm (7 min)
- Correctness of Kruskal's Algorithm (9 min)
- Implementing Kruskal's Algorithm via Union-Find I (9 min)
- Implementing Kruskal's Algorithm via Union-Find II (13 min)
- MSTs: State-of-the-Art and Open Questions [Advanced - Optional] (9 min)
- Application to Clustering (11 min)
- Correctness of Clustering Algorithm (9 min)
- Advanced Union-Find
- Lazy Unions [Advanced - Optional] (10 min)
- Union-by-Rank [Advanced - Optional] (12 min)
- Analysis of Union-by-Rank [Advanced - Optional] (14 min)
- Path Compression [Advanced - Optional] (14 min)
- Path Compression: The Hopcroft-Ullman Analysis I [Advanced - Optional] (9 min)
- Path Compression: The Hopcroft-Ullman Analysis II [Advanced - Optional] (11 min)
- The Ackermann Function [Advanced - Optional] (16 min)
- Path Compression: Tarjan's Analysis I [Advanced - Optional] (14 min)
- Path Compression: Tarjan's Analysis II [Advanced - Optional] (13 min)
- Introduction and Motivation (9 min)
- Problem Definition (10 min)
- A Greedy Algorithm (16 min)
- A More Complex Example (4 min)
- Correctness Proof I (10 min)
- Correctness Proof II (12 min)
- Problem Set 2
- Optional Theory Problems
- Programming Assignment 2
- Introduction To Dynamic Programming
- Introduction: Weighted Independent Sets in Path Graphs (7 min)
- WIS in Path Graphs: Optimal Substructure (9 min)
- WIS in Path Graphs: A Linear-Time Algorithm (9 min)
- WIS in Path Graphs: A Reconstruction Algorithm (6 min)
- Principles of Dynamic Programming (7 min)
- The Knapsack Problem (9 min)
- A Dynamic Programming Algorithm (9 min)
- Example [Review - Optional] (12 min)
- Optimal Substructure (13 min)
- A Dynamic Programming Algorithm (12 min)
- Problem Definition (12 min)
- Optimal Substructure (9 min)
- Proof of Optimal Substructure (6 min)
- A Dynamic Programming Algorithm I (9 min)
- A Dynamic Programming Algorithm II (9 min)
- Problem Set 3
- Optional Theory Problems
- Programming Assignment 3
- Overview
- Single-Source Shortest Paths, Revisited (10 min)
- Optimal Substructure (10 min)
- The Basic Algorithm I (8 min)
- The Basic Algorithm II (10 min)
- Detecting Negative Cycles (9 min)
- A Space Optimization (12 min)
- Internet Routing I [Optional] (11 min)
- Internet Routing II [Optional] (6 min)
- Problem Definition (7 min)
- Optimal Substructure (12 min)
- The Floyd-Warshall Algorithm (13 min)
- A Reweighting Technique (14 min)
- Johnson's Algorithm I (11 min)
- Johnson's Algorithm II (11 min)
- Problem Set 4
- Optional Theory Problems
- Programming Assignment 4
- Overview
- Polynomial-Time Solvable Problems (14 min)
- Reductions and Completeness (13 min)
- Definition and Interpretation of NP-Completeness I (10 min)
- Definition and Interpretation of NP-Completeness II (7 min)
- The P vs. NP Question (9 min)
- Algorithmic Approaches to NP-Complete Problems (12 min)
- The Vertex Cover Problem (8 min)
- Smarter Search for Vertex Cover I (9 min)
- Smarter Search for Vertex Cover II (7 min)
- The Traveling Salesman Problem (14 min)
- A Dynamic Programming Algorithm for TSP (12 min)
- Problem Set 5
- Optional Theory Problems
- Programming Assignment 5
- Overview
- A Greedy Knapsack Heuristic (14 min)
- Analysis of a Greedy Knapsack Heuristic I (7 min)
- Analysis of a Greedy Knapsack Heuristic II (9 min)
- A Dynamic Programming Heuristic for Knapsack (11 min)
- Knapsack via Dynamic Programming, Revisited (10 min)
- Analysis of Dynamic Programming Heuristic (15 min)
- The Maximum Cut Problem I (8 min)
- The Maximum Cut Problem II (9 min)
- Principles of Local Search I (8 min)
- Principles of Local Search II (10 min)
- The 2-SAT Problem (14 min)
- Random Walks on a Line (16 min)
- Analysis of Papadimitriou's Algorithm (14 min)
- Stable Matching [Optional] (15 min)
- Matchings, Flows, and Braess's Paradox [Optional] (13 min)
- Linear Programming and Beyond [Optional] (11 min)
- Epilogue (1 min)
- Problem Set 6
- Optional Theory Problems
- Programming Assignment 6
Final Exam
Post-Course Survey
Instructors
Stanford Frequently Asked Questions (FAQ's)
No. If you don’t have any prior programming experience, you will not be able to follow.
Yes, there are problem sets with multiple-choice questions and six programming assignments for practice.
The final assessment exam contains multiple-choice questions covering all the topics covered throughout the course.
The assignments do not have a due date. You can take your time to complete them and turn them in.
The instructor for this course is Tim Roughgarden, Professor of Computer Science at Stanford University.
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