Masters vs Undergrad

Before starting my master's degree in robotics at UPenn, I had many unanswered questions about what a master's degree would be like. I was certain that it would be considerably more difficult than undergrad but inspite of asking many current masters' students, I didn't have an idea of what to expect. As with many considering a master's degree abroad, my main concerns were not knowing how hard the coursework was going to be and adapting to the cultural differences in the US. I was also anxious about not having an exit plan figured out before starting my degree. Fortunately, each of my concerns were unfounded throughout the course of my first semester, and with this blog post, I want to share the key differences I experienced between undergraduate education in India with my first semester at UPenn.


For my first semester, I registered for the three courses - Introduction to Robotics, Computer Vision and Computational Photography, and Machine Learning. Though these courses are considered to be relatively heavy (even by UPenn's standards), I found it hard to believe that three courses with ten hours of classes a week could be stressful, especially when we were required to handle more than double the number of courses in undergrad.

It didn't take long to realise why though. Compared to undergrad in India, master's coursework in the US places a heavy emphasis on homework and assignments. The weekly individual assignments for each course I had in my first semester at UPenn are comparable to the monthly "group" programming assignments we used to have in undergrad. Evaluation policies for assignments are strict and late submissions for two of my courses were penalized at a 25% deduction in the maximum score per late day.

A master's degree will test the limits of your throughput and the rapid succession of assignment deadlines can easily lead to burnout. With the end of semester final project submissions, the last four weeks of the semester are particularly brutal for everyone. I don't think I would've made it through without compromising on sleep and inadequately compensating for it with the free coffee available at the Grad Students' Center. That said, the struggle wasn't for nothing. I feel that the knowledge I've gained through this process is much more durable than the kind gained for getting through a couple of semester exams. As an aside, the three courses I took this semester were not math heavy beyond needing to know some linear alegbra properties and solving the occasional differential equation.

The grading itself is not difficult - in most courses, scoring above the median will land you an A and scoring below will give you a B. C's and D's are only given if you neglect to turn in multiple assignments. Since the level of competition is high, even crossing the median score can be a challenge!


The atmosphere at UPenn is more collaborative than what I had experienced before. You will frequently need to approach professors, TA's, and other students for help. Collaboration in projects here goes beyond the division of labour of work as it even involves finding solutions to problems for which there aren't any right answers. I also found other students to be more competitive and motivated. It's the norm to spend several hours a day banging one's head against figurative walls. A master's degree is also a significant financial commitment and it also comes with the opportunity cost of working for two years. This tradeoff is a good reason to be diligent about the coursework.

The pace of instruction is faster as well and it requires much more attentiveness in class. In the first four weeks of the Machine Learning course at UPenn, I learned more than I had in the entire semester of the Machine Learning course in undergrad.

Free Time

Due to the heavy coursework, household chores (especially cooking!), and part-time TA jobs that most students end up doing, free time ends up becoming quite limited. One of my gripes so far is that I haven't been able to explore research outside of my coursework despite the ample opportunities for the taking at UPenn's numerous robotics labs. It is possible to manage three courses with research, but I feel taking an independent study or equivalent course is more sustainable.

Preparation Required

I think it's a good idea to brush up on the tools you will need before starting a master's degree in engineering. First year fundamental math courses are important. Gilbert Strang's textbook and OCW series are good resources for linear algebra. There is a good chance that you will need to use LaTeX for homework and scientific software such as MATLAB and Python with scipy, numpy, sklearn, and pytorch among many others. Learning to use these tools effectively can save you a lot of time later. Though I feel it isn't required to prepare beforehand, you can find most of the course material of graduate courses online if you wish to preview the course content. However, the best way to know what you're getting into is by asking a helpful senior at the college you're planning to attend :-)