Listed below are all 33 classes I’ll be covering. Will I fail? It’s definitely a possibility-people a lot smarter than myself struggle through immense workloads at institutions like MIT, and I’m attempting to learn the same material at 4x the speed, without the benefit of instructors.Īll I can promise is to share what I find with you. I’m embarking on this experiment because I want to show that learning doesn’t require acceptance boards and SAT tests, thousands of dollars in debt, or even the 4-year pace most students assume is necessary to learn a subject. But, I didn’t want to invest four years of my life and hundreds of thousands of dollars to learn it. I’ve always wanted to speak that language. Fortunes have been made and revolutions sparked on lines of code. The largest transformations of our world are being written in code, and advancements in artificial intelligence allow us to use computers to understand what it means to be human.īeyond the poetry of the machine, computer science is also immensely practical. From finance to Facebook, algorithms are the hidden language that underlies most of our life. My goal for this project was to see if it would be possible–to push the expectations for how long, how costly and how conventionally an education must be obtained.Ĭomputers have always fascinated me. I did my undergrad in university and I don’t regret it. Do you believe everyone shouldn’t go to university and do this instead? Of course not.I believe the original grading was fair, if imperfect, but these later calculations show the impact of my decision to use part marks. I’ve gone through and recalculated under these more severe restrictions and it would put the final exams for 18.01, 5.111, 18.03, 6.002 and 6.013 below the passing threshold (the other 28 classes were either unaffected or stayed above passing). I believe the provided grading is arguably a fair one, but if you wanted to take the strong position that any mistake (such as forgetting to carry a minus sign) invalidates an entire question, this would reduce some of my grades. In many of the exams that had lengthy calculations required, I allowed for part marks provided the concepts taught in the course were applied correctly. I encourage anyone to check out my actual exam results and compare them against the solutions. However, most of the exams are quantitative with solution sets that have grading rubrics, so it limits the error somewhat. Admittedly, this introduces some degree of error over having a professor grade my work. However if you followed this order serially you wouldn’t miss any prerequisites. What order did you do the classes in? In the order listed below.Please check it out if you’re unsure for each class. Did you use textbooks or lecture videos? Everything is in the “See More…” pane for each class.(Note: This link has been updated as the old one was dead, however I haven’t checked whether there were changes made to the CS curriculum since I did the MIT Challenge) Check here for MIT’s actual 4-year CS curriculum as a comparison to my own. The number of credit hours is the same though. I did the exams and programming projects for a curriculum that is very similar to MIT’s own (I had to swap some lab classes and humanities requirements for other classes). Did you do everything an MIT student does? No.See the talk above for my motivation to do the entire education without credit. How much did I work during the challenge? In the beginning roughly 60 hours/week.Click on “See More…” for any class to access my exams, official solutions or code I wrote. I finished on September 26, 2012, just under 12 months after beginning October 1st, 2011. UPDATE: The MIT Challenge is now complete. Any commercial use of the data or materials provided at this website is not allowed.Over the next 12 months, I’m going to learn the entire 4-year MIT curriculum for computer science, without taking any classes. There is no guarantee that the data is free of errors. The OSF project hosts the relevant materials. This special issue solicits papers about machine learning approaches for all aspects of soccer. In conjunction with this special issue, we organize a machine learning challenge task where the goal is to predict the outcomes of future matches based on a data set of over 200,000 soccer matches from soccer leagues around world. Data science and analytics are being more frequently employed on both the club and national levels to improve performance, equipment, marketing, scouting, etc. The Machine Learning Journal (Springer) invites submissions of original contributions to machine learning research for soccer analytics.
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