My first year at McMaster University has been relatively quiet. The vast majority of my time was spent working in my modest room near the campus. Engulfed with coursework and research, I wish I had spent more time roaming around Hamilton. Perhaps visiting Hamilton's purportedly "world-famous" waterfalls would have created more meaningful memories.
Still, a great GPA is a some consolation. As per my course requirements I took the following courses (i) graduate computational complexity (ii) advanced topics in data management (iii) algebraic methods in computer science and (iv) software design. The course on graduate computational complexity was mind-bending and really exposed me to new ideas and interesting research directions. Advanced topics in data management focused on data cleaning, data mining and data privacy, a significant overlap with my research. Algebraic methods was assignment focused and completely math-y, while we had a lot more freedom with the project-based software design course. Overall, I think my course choices were great.
I also audited the graduate algorithms and data structures class and partially audited an introduction to graduate statistics course with the Math department. The algorithms course was fantastic and really a step up from the typical undergrad-level algorithms courses that you find in most universities. Unfortunately, I started auditing the statistics course too late in the term and could not make too much sense of it halfway through. I'll audit it from the beginning in my second year.
It was also my first time TA-ing a course- web systems and web computing, which is offered to 3rd/4th year undergrads and grad students. I actually enjoyed giving the 45 min lectures, covering overviews of topics such as Java web frameworks, front-end frameworks, databases, etc. However, marking student assignments is a real chore.
In terms of hobbies, I started teaching myself how to play the guitar, probably one of the best decisions I've ever made. During the frigid winter months, nothing beats strumming a few tunes. Occasionally, I would go to the MacBeat club meetings and jam with a few guitarists on Thursday's.
Yet, most of my Thursday's were actually channelled towards chess. As a beginner, all those losses by various members of the chess club are hard to bear. I'm trying to learn from them and get better. But chess can get frustrating at times (I hate losing!). It's not just chess- combined with coursework and research, one really needs some sort of an outlet to combat stress. Luckily, I'm swimming regularly...
I've made a few good friends this year, so I'm quite looking forward to spending more time with them next year, and not staying holed up in my room all the time. Still, I wish the grad CS cohort was larger. As it is, it is difficult to find sociable CS students, haha!
Grad life is vastly different from my undergrad days. There is a lot more studying and a lot less socializing. Its also strangely difficult to identify with the undergrads and their lifestyle. I guess I have changed a bit since my undergrad days.
Targets for next year
Still, a great GPA is a some consolation. As per my course requirements I took the following courses (i) graduate computational complexity (ii) advanced topics in data management (iii) algebraic methods in computer science and (iv) software design. The course on graduate computational complexity was mind-bending and really exposed me to new ideas and interesting research directions. Advanced topics in data management focused on data cleaning, data mining and data privacy, a significant overlap with my research. Algebraic methods was assignment focused and completely math-y, while we had a lot more freedom with the project-based software design course. Overall, I think my course choices were great.
I also audited the graduate algorithms and data structures class and partially audited an introduction to graduate statistics course with the Math department. The algorithms course was fantastic and really a step up from the typical undergrad-level algorithms courses that you find in most universities. Unfortunately, I started auditing the statistics course too late in the term and could not make too much sense of it halfway through. I'll audit it from the beginning in my second year.
It was also my first time TA-ing a course- web systems and web computing, which is offered to 3rd/4th year undergrads and grad students. I actually enjoyed giving the 45 min lectures, covering overviews of topics such as Java web frameworks, front-end frameworks, databases, etc. However, marking student assignments is a real chore.
In terms of hobbies, I started teaching myself how to play the guitar, probably one of the best decisions I've ever made. During the frigid winter months, nothing beats strumming a few tunes. Occasionally, I would go to the MacBeat club meetings and jam with a few guitarists on Thursday's.
Yet, most of my Thursday's were actually channelled towards chess. As a beginner, all those losses by various members of the chess club are hard to bear. I'm trying to learn from them and get better. But chess can get frustrating at times (I hate losing!). It's not just chess- combined with coursework and research, one really needs some sort of an outlet to combat stress. Luckily, I'm swimming regularly...
I've made a few good friends this year, so I'm quite looking forward to spending more time with them next year, and not staying holed up in my room all the time. Still, I wish the grad CS cohort was larger. As it is, it is difficult to find sociable CS students, haha!
Grad life is vastly different from my undergrad days. There is a lot more studying and a lot less socializing. Its also strangely difficult to identify with the undergrads and their lifestyle. I guess I have changed a bit since my undergrad days.
Targets for next year
- Conduct meaningful research and publish a paper in a top conference or journal.
- Audit the graduate statistics course with the Math department.
- Complete reading the books: PRML by Bishop and PGM's by Koller.
- Complete the MOOCs: Machine Learning by Ng and Learning from Data by Yaser.
- Focus on my own side-project.
- Participate in at least 1 Kaggle competition.
- Explore Hamilton's waterfalls.
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