MIT Challenge

I completed this class in July as part of a pilot experiment to see if I was up to doing the entire program. The course is brilliant, Prof. Walter Lewin does an amazing job bringing physics to life without sacrificing intellectual rigor. Note: This course was removed by MIT, you can access a different version here ^[4]. Scott’s rating: 5/5

This was the first official class I completed for the MIT Challenge. Solid lectures gave me a good sense of the basics of calculus starting from limits and going up to more advanced integration.
Scott’s rating: 4/5

This class extends the basic principles of single-variable calculus into more than one dimension. Very useful to know for understanding electricity, magnets, light or anything involving 3D objects.
Scott’s rating: 4/5

Another class taught by Prof. Walter Lewin, the demonstrations are amazing. Rigorous for those who want the technical details, but still approachable, discussing everyday concepts like rainbows and metal detectors. Note: The original course I took was removed by MIT. Here is a replacement course ^[19].
Scott’s rating: 5/5

This is a good class, but I don’t recommend taking it if you have no programming background, since it dives right into object-orientation with relatively little explanation. I really enjoyed the blending of more advanced concepts from AI and state machines into the core course. The course notes are free and excellent, although a lot of the focus is on robotics which use specialized equipment I don’t have access to. (As a side note, many people have pointed out that the final exam I wrote wasn’t fully comprehensive, you can read my in-depth reply here ^[25]) Note: The original course I took has been switched by MIT. Here is a replacement ^[26].
Scott’s rating: 4/5

Good introduction to quantum chemistry, Lewis diagrams, acid/base and redox reactions. Also includes a more advanced section on transition metals and crystal field theory. Unfortunately the class did not have a viable final exam, so I was forced to use my practice midterms as the basis of evaluation.
Scott’s rating: 4/5

Excellent biology course which explores biology from an experimentalist’s viewpoint, giving a grounding in the basics of biochemistry, immunology, genetics and ecology. Textbook is a good supplement as well, also showing the experimental mindset leading to the discoveries.
Scott’s rating: 4/5

An extension of calculus, differential equations allow you to model and analyze complex systems. They play a particularly important role in electrical engineering where they (and their discrete counterparts) are essential for understanding circuits, signals and systems. The video lectures associated with this class have significant deviations from the rest of the course content, which caused me to waste some time.
Scott’s rating: 3/5

This was a trickier class to take on since there isn’t a class for it in MIT’s OCW. Learning it meant sifting through the mostly hidden recesses of MIT’s public archives to try to find notes and exams. A consequence of that is that filter design, a major topic, was completely missing in the notes and I was unable to complete the labs (6 & 7) or the exam sections that dealt with it. There was no final exam, so I completed all three midterms and graded them collectively.
Scott’s rating: 3/5

Graphs, number theory, proofs, probability and counting are just a few of the topics covered in this excellent class. There aren’t any video lectures, but the free course textbook is incredible and engaging. There is also a huge amount of resources to practice the problems, offering interesting challenges.
Scott’s rating: 5/5

An introduction to algorithms and complexity theory. The course provides a good opportunity to apply algorithmic design concepts (dynamic programming, greedy algorithms, etc.) to actual programming problems, giving this class a concreteness that was missing in 6.042J. Unfortunately, I did not realize the final exam wasn’t comprehensive before writing it, so I’ve also included my quizzes I had done for practice. I completed most of the assignments, however as there were no solutions, I have left them ungraded.
Scott’s rating: 4/5

Excellent video lectures and abundant materials made this one of the more approachable courses in OCW’s catalog. Prof. Gilbert Strang gives fluent, easily understood lectures. My first exam for this class was only a 56%, so I decided to take another day to write the higher-scoring exam I posted below.
Scott’s rating: 5/5

A course in probability, it goes into the foundations of probability using calculus to analyze more difficult probabilistic questions than those posed in 6.042J. The textbook is designed for this course specifically, so it is extremely useful.
Scott’s rating: 4/5

This course covers the basics of digital and analog circuits. The exam was quite challenging and it marked the first exam where I actually ran out of time running it. Unfortunately the official solutions are MIT-access only, so I can’t upload them here as a comparison. This class serves as a basis for many of the later EE classes, so I’m sure I’ll get another chance to really be tested on the core concepts.
Scott’s rating: 4/5

One of my favorite courses thus far! The class explores some of the more advanced concepts in algorithms not found in 6.006 such as proving particular problems are NP-Complete, approximation algorithms, encryption, sublinear algorithms and parallel processing. I had to be careful though since the OCW version of this course is prior to MIT’s curriculum change where 6.006 was added, so it covers substantially easier material than the current versions.
Scott’s rating: 5/5

Great class that covered a survey of different AI techniques including search, constraint propagation, neural networks, kernel machines, concept learning and natural language processing. I also completed 6 assignments for this class which required me to learn the programming language Scheme. One of the challenges with writing exams for this class was the variety in the syllabi, since so many different techniques were covered and the specifics differed from class to class.
Scott’s rating: 5/5

This class teaches the basics of signal and systems analysis, extending the mathematical techniques first discussed in 18.03. While useful for more than just circuits, the class is particularly important for understanding things like frequency modulation, audio filters, noise reduction and even serves as a basis for image analysis and manipulation. This also was the first exam which I failed initially, luckily I improved and scored a 69% on my second attempt. Note: The original course I took isn’t available anymore. Here is a replacement ^[84].
Scott’s rating: 4/5

Great class teaching the crossover between electrical engineering and computer science. The design project involved building a gate-level CPU specification, that is building the circuitry to allow for a computer to be built. The only weakness was the class’s relative lack of material, however the assignments and labs are strongly supported.
Scott’s rating: 4/5

Intro to sentential and predicate calculus, their derivation systems and metatheoretical proofs. I found the class rather dry and technical, but the class did offer good practice into more rigorous proof methods, as opposed to the more informal styles taught in computer science classes. Unfortunately, the exam answers I used to correct my paper aren’t public, so I can’t post them here.
Scott’s rating: 3/5

I had already taken microeconomics when doing my business degree, so the vast majority of this course was a recap. However, since MIT has a much stronger math basis than my alma mater, it was refreshing to see more rigorous technical definitions of many of the concepts. I spent a little under four days on this course, so most of my errors were from forgetting technical definitions and formulas since none were provided on the exam.
Scott’s rating: 3/5

A useful class teaching the basics of computer networks, encryption, client-server paradigm and the Internet. I found some of the class repetitive after 6.004 and 6.02, which had overlapping content, but it was still useful. I made the mistake of not focusing enough of the reading assignments since that’s where a bulk of the learning comes from.
Scott’s rating: 4/5

Without a doubt, the hardest class I’ve taken in the program thus far. The class makes heavy use of vector calculus, transforms and complex notation in addition to generally extending the principles of electromagnetism taught in 8.02. A strong foundation in the topics of 6.003, 6.002, 18.02, 18.03 and 8.02 is a good idea if you want to attempt this class. I only passed the exam just barely, so it’s definitely a topic I’d like to revisit and refine at some point in the future, since it expressed a lot of interesting ideas.
Scott’s rating: 3/5

I had also done macroeconomics before when I did my undergraduate degree, so this was a recap mostly. That said, I thought the MIT version of the course and supporting material were much more accessible. It’s a great class to take if you want to understand the economy as a whole. Note: The original course I took was switched by MIT. Here is a replacement ^[109].
Scott’s rating: 3/5

This class was entirely based on 8 homework assignments, so the amount of testing material I went through was weaker than many others, unfortunately. That said, I loved this class, as Godel’s incompleteness theorems represent one of the most profound ideas I’ve ever seen in a class.
Scott’s rating: 5/5

This class was an extension of 6.003, Signals and Systems. It takes the basic concepts of signal analysis and filters and extends it to state-machine representations and stochastic signals. The inclusion of random signals is very useful for handling noise, so many of the concepts in this class are more practical than their ideal counterparts in 6.003. The free textbook/course notes was very useful.
Scott’s rating: 4/5

Modal logic is the logic of possibility and necessity and extends the basic predicate calculus. It’s an interesting logic to study because it can represent many diverse systems of logic, depending on the axioms taken (deontic, alethic, tense, etc.). The final exam was a take-home final, so I completed it over the course of two days.
Scott’s rating: 3/5

Intro economics courses typically simplify analysis by assuming perfect competition or monopoly. This class removes that assumption and considers how firms compete strategically against one another. This class really sparked my interest in learning more about game theory. Unfortunately I didn’t realize the sample final exam was fairly limited as a coverage of the content, but it was the best I could do.
Scott’s rating: 3/5

The content of this class was very similar to 14.20, although it focused more on the historical context and political framework than the game-theory side. An interesting class, the textbook reading sections were quite lengthy (nearly 900 pages) however the level of mathematical analysis was light, so it made for easy reading.
Scott’s rating: 3/5

Given the nature of my challenge, this was a fun class since it explored the various theories of education. Most of the class felt like a discussion in basic econometrics, as it appears the literature is inconclusive on many core ideas (are smaller classes better? do voucher programs work?). The class also explored the differences between the standard human-capital theory of education versus the signaling explanation, although, once again, the literature presented was mostly inconclusive.
Scott’s rating: 3/5

This is MIT’s required software development class. It was a tough one, not because it is too intellectually demanding, but because of the amount of work. It consists of 3 team projects, 3 individual projects and several labs (assignments, in their own right). I was hesitant about taking this class on because the time constraints of the challenge make it difficult to take on classes like this, however, in the end I figured the only way was simply to do all the work.

This class teaches the basics of how to use computer algorithms to see, primarily under the context of guiding a robot to perform actions based on those images. Topics include binary image processing, edge detection, Gaussian extended surfaces, orientation and photogrammetry. The course was evaluated by two take home quizzes and counted as one of two graduate-level classes I took in the challenge.

This class teaches the basics of computer graphics, primarily ray tracing, although scanline methods are taught. The evaluation was based entirely on assignments (there were two minor quizzes, but they had no solutions so I didn’t use them). Starting with a basic ray caster and flat shading, the assignments progressively built up the ray tracer to include matte shading, glossy reflections, cast shadows, transparent surfaces with indices of refraction, mirror reflection and procedural texturing. A project was also done on creating a grid acceleration data structure, which has no visual effect but greatly speeds up the relatively slow ray tracing approach to graphics.

This class wasn’t an MIT course but a graduate class in the theory of computation taught by Cal Newport at Georgetown University. This was a topic I had really wanted to study after enjoying both of MITs algorithm courses, but the MIT complexity theory course lacked any evaluation tools I could use. Some interesting ideas from the course are which types of problems can be solved by algorithms (meaning there is a process for solving any instance of the problem) and the restrictions on the speed and memory required to solve certain classes.