I was an economics fanatic in my past life. Still am, but after concentrating most of my time pursuing that front, I figured it’s time to branch out. My love for economics still lives on, but, spurred by the can-do spirit of inventing the future, I’ve decided to self-study MIT’s entire CS curriculum—hopefully finishing it under a year.

Profound autodidacts have inspired me for the longest of times—Musk, Munger, Franklin, and so on. They make the impossible, possible. In a world where formal education takes precedence, these people have taken it upon themselves to learn whatever’s needed to reach their goals. Thanks to the internet, really, what’s left for us to do is to carefully think long and hard about what project to pursue before dedicating ourselves to achieve it. I’ve found my answer in self-learning MIT CS.

Big thanks to Scott Young¹! He’s the main inspiration for undertaking this specific challenge. He did it in a year, and he showed what’s possible. He’s passed the baton to all of us, so I’ve got to at least try to push the bar. I’m not alone in my mission—I’ve recruited a couple friends as crazy as me to join me in this endeavor for motivation and accountability. Hopefully we come out of this alive.

You may ask what are my motivations in this. Partly, it’s because I’ve always been fascinated with how technology works and, increasingly, how it shapes our world. We really take it for granted—from easily wiring my friends money to tapping my screen to have food delivered—if we really sit and think about it, it’s almost bewildering, I’m sure 16th century time travelers agree. In other ways, tech is a tool in the sandbox. I’ve got many ideas that I wish to realize, and tech is one means of making it a reality. We’ll see in time to come if my projects become successful, until then—don’t blink!

What courses will I take?

So, the way MIT OCW² works is that it’s just a massive archive of lectures. It isn’t pre-organized for you in the sense that you could simply search for MIT’s CS curriculum, and it’ll lay out step by step what you have to do. Instead, I’ll have to piece together the sequence of lectures to take. Thanks to references on the internet³ and taking inspiration from Scott’s course load, I’ve come up with the following sequence (each linked with its respective OCW course for your perusal).

Full self-reconstructed MIT EECS syllabus:

• Intro to CS and Python https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/
• Intro to C and C++ https://ocw.mit.edu/courses/6-s096-introduction-to-c-and-c-january-iap-2013/
• Single Variable Calc https://ocw.mit.edu/courses/18-01-single-variable-calculus-fall-2006/
• Math for CS https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-fall-2010/
• Intro to Computational Thinking and Data Science: https://ocw.mit.edu/courses/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/
• Computer System Architecture: https://ocw.mit.edu/courses/6-823-computer-system-architecture-fall-2005/
• Algebraic Combinatorics: https://ocw.mit.edu/courses/18-212-algebraic-combinatorics-spring-2019/
• Multivariable Calculus: https://ocw.mit.edu/courses/18-02-multivariable-calculus-fall-2007/
• Object-Oriented Programming: https://ocw.mit.edu/courses/6-088-introduction-to-c-memory-management-and-c-object-oriented-programming-january-iap-2010/
• Effective Programming in C and C++ https://ocw.mit.edu/courses/6-s096-effective-programming-in-c-and-c-january-iap-2014/
• Intro to Algorithms https://ocw.mit.edu/courses/6-006-introduction-to-algorithms-spring-2020/
• Intro to Probability and Statistics https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2022/
• Computer Networks https://ocw.mit.edu/courses/6-829-computer-networks-fall-2002/
• Linear Algebra https://ocw.mit.edu/courses/18-06sc-linear-algebra-fall-2011/
• Intro to Programming in Java https://ocw.mit.edu/courses/6-092-introduction-to-programming-in-java-january-iap-2010/
• Operating System Engineering https://ocw.mit.edu/courses/6-828-operating-system-engineering-fall-2012/
• Software Engineering Concepts https://ocw.mit.edu/courses/16-355j-software-engineering-concepts-fall-2005/
• AI https://ocw.mit.edu/courses/6-034-artificial-intelligence-spring-2005/
• Design and Analysis of Algorithms https://ocw.mit.edu/courses/6-046j-design-and-analysis-of-algorithms-spring-2015/
• Discrete Stochastic Processes https://ocw.mit.edu/courses/6-262-discrete-stochastic-processes-spring-2011/
• Database Systems https://ocw.mit.edu/courses/6-830-database-systems-fall-2010/
• Distributed Computer Systems Engineering https://ocw.mit.edu/courses/6-824-distributed-computer-systems-engineering-spring-2006/
• Digital Signal Processing https://ocw.mit.edu/courses/res-6-008-digital-signal-processing-spring-2011/
• Introduction to Robotics https://ocw.mit.edu/courses/2-12-introduction-to-robotics-fall-2005/
• Advanced Algorithms https://ocw.mit.edu/courses/6-854j-advanced-algorithms-fall-2008/
• Machine Learning https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/
• Parallel Computing https://ocw.mit.edu/courses/18-337j-parallel-computing-fall-2011/
• Data Mining https://ocw.mit.edu/courses/15-062-data-mining-spring-2003/
• Statistics for Applications https://ocw.mit.edu/courses/18-443-statistics-for-applications-spring-2015/
• Computational Cognitive Science https://ocw.mit.edu/courses/9-52-c-computational-cognitive-science-spring-2003/
• Matrix Methods in Data Analysis, Signal Processing, and Machine Learning https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/
• Mathematics of Machine Learning https://ocw.mit.edu/courses/18-657-mathematics-of-machine-learning-fall-2015/
• Time Series Analysis https://ocw.mit.edu/courses/14-384-time-series-analysis-fall-2013/
• Language Processing https://ocw.mit.edu/courses/9-591j-language-processing-fall-2004/
• Topics in Math with Applications in Finance https://ocw.mit.edu/courses/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/
• Advanced NLP https://ocw.mit.edu/courses/6-864-advanced-natural-language-processing-fall-2005/
• Intro to Convex Optimization https://ocw.mit.edu/courses/6-079-introduction-to-convex-optimization-fall-2009/
• Cognitive Robotics https://ocw.mit.edu/courses/16-412j-cognitive-robotics-spring-2016/
• Bioinformatics and Proteomics https://ocw.mit.edu/courses/6-092-bioinformatics-and-proteomics-january-iap-2005/

That’s 38 full courses. Boy, I’ve got a lot to do.

Before we begin,

I’ll blog every course and keep you guys posted on each development. I’ll link my notes and answers to exams for reference, if any of you are crazy enough like me to attempt such a feat, or if you’re just curious.

I’ll define ‘passing’ each course as scoring a B or higher on each exam, and I’ll replicate each exam as closely as possible—if the rules say no calculators, no calculators it is; if the rules say 3 hours, 3 hours it is. Thanks to the quantitative nature of computer science, a lot of the marking will be impartial (fortunately, no overestimations of my writing if this were an English test). I’m sure there will be some variability somehow, but by following the exam instructions to a tee, I hope to minimize this.

Finally, the true test of knowledge is in application. On the side, I’m also learning full stack development by myself. Hopefully by combining my knowledge in full stack and MIT’s CS education, I’ll be able to bring to life some of my crazy ideas. I won’t tell too much for now—stay tuned!

1/38 6.0001 Introduction to Computer Science and Python

The first course!

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1. Here’s a link to Scott’s original MIT challenge blogpost: https://www.scotthyoung.com/blog/myprojects/mit-challenge-2/ ↩︎
2. Peruse any available course on MIT OCW here! (not limited to CS): https://ocw.mit.edu/ ↩︎
3. I mostly followed recommendations from this Redditor for a more comprehensive CS-centric syllabus. I found a lot that I’d already learned a lot Scott’s original course load since I was a STEM student back in high school. Also, I ran my courses by GPT4 and it said my course sequence looked pretty neat. Feel free to suggest any modifications!: https://laconicml.com/computer-science-engineer-mit-university/ ↩︎