My completed coursework for 9 MIT courses taken through MIT OpenCourseWare and MIT Open Learning Library. Each repository covers the exact same coursework required of MIT students for full marks, with the exception of 6.034 which only includes the coding assignments. All solutions are my own.
| Name | Repository | Homepage |
|---|---|---|
| 18.01SC Single Variable Calculus | Repository | OCW Page |
| 18.02SC Multivariable Calculus | Repository | OCW Page |
| 18.06SC Linear Algebra | Repository | OCW Page |
| 6.0001 Intro to CS & Programming in Python | Repository | OCW Page |
| 6.0002 Intro to Computational Thinking & Data Science | Repository | OCW Page |
| 6.006 Introduction to Algorithms | Repository | OCW Page |
| 6.034 Artificial Intelligence | Repository | OCW Page |
| 6.036 Introduction to Machine Learning | Repository | Open Learning Page |
| 6.042J Mathematics for Computer Science | Repository | OCW Page |
| Course | Type | Count |
|---|---|---|
| 18.01SC Single Variable Calculus | Sessions | 75 |
| Problem Sets | 11 | |
| Exams | 4 | |
| Final Exam | 1 | |
| 18.02SC Multivariable Calculus | Sessions | 83 |
| Problem Sets | 12 | |
| Exams | 4 | |
| Final Exam | 1 | |
| Practice Exams | 4 | |
| Practice Final Exam | 1 | |
| 18.06SC Linear Algebra | Sessions | 31 |
| Exams | 3 | |
| Final Exam | 1 | |
| 6.0001 Intro to CS & Programming in Python | Problem Sets | 6 |
| 6.0002 Intro to Computational Thinking & Data Science | Problem Sets | 5 |
| 6.006 Introduction to Algorithms | Problem Sets | 7 |
| Quizzes | 2 | |
| Final Exam | 1 | |
| 6.034 Artificial Intelligence | Labs | 6 |
| 6.036 Introduction to Machine Learning | Labs | 7 |
| 6.042J Mathematics for Computer Science | Problem Sets | 12 |
| Recitations | 23 | |
| Midterm | 1 | |
| Midterm Practice Problems | 1 | |
| Finals | 4 |
| Course | Assessment | Grade |
|---|---|---|
| 18.01SC Single Variable Calculus | Exam 1 | 7/9 |
| Exam 2 | 7/7 | |
| Exam 3 | 4/8 | |
| Exam 4 | 4/4 | |
| Final Exam | 18.2/20 | |
| 18.02SC Multivariable Calculus | Exam 1 | 15/15 |
| Exam 2 | 11/15 | |
| Exam 3 | 12/12 | |
| Exam 4 | 11/11 | |
| Final Exam | 32/32 | |
| Practice Exam 1 | 99/100* | |
| Practice Exam 2 | 93/100* | |
| Practice Exam 3 | 97/100* | |
| Practice Exam 4 | 98/100* | |
| Practice Final Exam | 95/100* | |
| 18.06SC Linear Algebra | Exam 1 | 88/100** |
| Exam 2 | 94/100** | |
| Exam 3 | 80/100** | |
| Final Exam | 93/100** | |
| 6.006 Intro to Algorithms | Quiz 1 | 79/120*** |
| Quiz 2 | 102/120 | |
| Final Exam | 160/180 | |
| 6.042J Math for CS | Midterm Practice Problems | 107/125 |
| Midterm | 97/120 | |
| 2004 Final | 100/100 | |
| 2006 Final | 100/100 | |
| 2008 Final | 100/100 |
*Practice exams were not graded at the time of completion (~2021). To be more precise, they were graded in 2026 using Claude Opus 4.6 with extended thinking enabled, cross-referenced with the official solutions.
**Exams were not graded at the time of completion (~2024). To be more precise, they were graded in 2026 using Claude Opus 4.6 with extended thinking enabled, cross-referenced with the official solutions.
***Quiz 1 scores were low across the board in the original Fall 2011 MIT class this course was recorded from. The course staff acknowledged this here: "The results are lower than what we thought... the mistake was on our part."
A 3D graphics engine with real-life spatial display using 2D screens and eye tracking. A childhood project built between the ages of 14 and 17 (2016-2019), inspired by Iron Man's holographic table.
Big number library with Karatsuba multiplication, Newton-Raphson division, and modular exponentiation. Applied to RSA encryption.
Balanced tree hierarchy from scratch powering a sweep line algorithm for O(n log n) wire crossing detection.
Shortest-path routing on the National Highway Planning Network with great-circle distance and KML visualization.
Bidirectional BFS on the 2x2x2 Rubik's cube using permutation group inverses.
DP implementation of the Avidan & Shamir seam carving algorithm.
Profiled an event-driven circuit simulator, identified a bottleneck in the O(n) priority queue, and replaced it with a min-heap built from scratch.
Forward and backward propagation with sigmoid activation through multi-layer networks.
Weighted voting, weight updates based on classification error, and best-classifier selection. Applied to congressional voting data.
Complete implementation of the uninformed-to-informed search taxonomy with heuristic verification.
CSP solver with forward checking and BFS-style singleton domain propagation.
k-NN classification using Hamming and Euclidean distance, and a Shannon entropy function for decision tree split quality.
Adversarial game search using negamax-style alpha-beta pruning.
Recursively builds AND/OR goal trees from production rules with pattern-variable unification.
Stochastic simulation of bacterial growth and natural selection with Monte Carlo trials and 95% confidence intervals.
Polynomial regression on US temperature data with train/test split demonstrating overfitting.
Monte Carlo simulation of robot vacuum cleaners with a multi-level OOP hierarchy and stochastic faulty robot variant.
Graph-based shortest path across the MIT campus using DFS with branch-and-bound pruning and an outdoor distance constraint.
News feed filtering with a deep OOP trigger hierarchy implementing strategy and composite design patterns, with a Tkinter GUI.
Recursive permutation generator connected to two encryption systems with class inheritance.
Word game with wildcard vowel substitution, hand management, and multi-hand orchestration.
I'd be very happy to discuss anything related to MIT OCW. Reach me at benjamin.jazayeri@gmail.com.