I am beyond grateful for the opportunities I got this semester as I TA’ed 3 courses while maintaining a full course load and this post just summarizes some of the ways I managed to do this while also talking about my experiences.
1 - Algorithms 1 ( CMPUT 204)
The first of two courses on algorithm design and analysis, with emphasis on fundamentals of searching, sorting, and graph algorithms. Examples include divide and conquer, dynamic programming, greedy methods, backtracking, and local search methods, together with analysis techniques to estimate program efficiency. Prerequisites: CMPUT 175 or 275 and CMPUT 272; one of MATH 100, 113, 114, 117, 134, 144, 154, or SCI 100.
2 - CMPUT 267 - Basics of Machine Learning
This course introduces the fundamental statistical, mathematical, and computational concepts in analyzing data. The goal for this introductory course is to provide a solid foundation in the mathematics of machine learning, in preparation for more advanced machine learning concepts. The learning outcomes are to become more comfortable with underlying concepts in machine learning, including how to formalize learning problems using probability and statistics; how models can be estimated from data; what sound estimation principles look like; how generalization is achieved; and how to evaluate the performance of learned models. Specific topics include: basic probability and optimization concepts, maximum likelihood, linear regression and polynomial regression, classification with logistic regression and regularization. Prerequisites: CMPUT 174 or 274; one of MATH 100, 114, 117, 134, 144, or 154. Corequisites: CMPUT 175 or 275; CMPUT 272; MATH 125 or 127; one of STAT 141, 151, 235, or 265, or SCI 151.
3 - MATH 117 - Honors Calculus I
Functions, continuity, and the derivative. Applications of the derivative. Extended limits and L’Hospital’s rule. Prerequisites: Mathematics 30-1 and Mathematics 31, or consent of the Department. Notes: (1) This course is designed for students with at least 80 percent in Pure Mathematics 30 or Mathematics 30-1 and Mathematics 31. (2) Credit can be obtained in at most one of MATH 100, 113, 114, 117, 134, 144, 154 or SCI 100. (3) Engineering students will receive a weight of 4.0 units for this course.
All of these courses were extremely interesting and fun to learn when I took them and hence why I really enjoyed TA’ing them. Some of the key points that I learnt (refercence)
Be Passionate! Wear your Geek on your sleeve!
Be creative & experiment with teaching!
Spend your time well! None of us, have enough of it! This was hard to maintain with my full course schedule but Time management is key.
If you don’t know the answer, find it out & follow-up with the student! I always made sure that I follow up with the student to make sure that they are learning something new or if I was incorrect with any answer
Take ownership of your space! Intrapersonal and physical alike! Students may not know as much as you want them too but they also learn a lot faster than you think they can!
Be respectful and empathetic! Try to connect with students and especially being an undergraduate TA helps achieve this faster as you are in the same boat as them.
Next semester I have decided to pursue Research instead of being a Teach Assistant so I am pretty excited for that.