STEM Student Spotlight: Daniel Kim, University of Alberta
Welcome to our STEM Student Spotlight series, where we highlight the incredible initiatives and achievements led by STEM students in Canada. Our mission is to empower and inspire the next generation of engineers and innovators by celebrating the achievements of student leaders. In today’s spotlight, we are highlighting the experience of Daniel Kim — a student attending the University of Alberta!
Daniel focused on the topic of Supervised Learning — where students learn the fundamentals of supervised learning and applying regression supervised learning techniques to predict parameters, quality metrics and performance indicators. They also learn about different regression types and scenarios where they can be applied.
Daniel first learned about the foundation of supervised machine learning and regression. Over the course of the program, Daniel also learned how to predict using different types of regression models:
“I mostly learned about the cost function, gradient descent and the hypothesis function and how they are all related.”
“I learned that there is a step called preprocessing where we transform the data for our model.”
Taking the techniques he learnedmost fromabove, daniel also learned about classification and learned to predict and manage imbalance datasets.
“Learning about binary classification, we saw that the model incorrectly predicted sometimes and that was because we had an imbalance dataset.”
Daniel reflected on how the skills learned during the time in this program are beneficial and particularly applicable to the field of computer science.
“As a university student studying computer sciences, I think this will greatly benefit me in terms of providing me with great background knowledge on the field(supervised learning) and will probably come into good use in my future studies.”
“I think learning the concept of machine learning itself could benefit your average teen or student even just knowing this as a bit of a background knowledge as computing science and machine learning will be a field that will surely continue to grow as we go into the future. Now for people like myself — young, university students studying computer sciences — this greatly benefits me in many different ways. Just to mention a few, just the concept itself is greatly beneficial since I will most likely be able to apply this knowledge into my future studies. It also would help in making decisions about which field of computing science that one would like to pursue since this would be a good introduction to one of the fields(machine learning) and you could either find that this works with you, or that this isn’t really for you. Either way, you will come out with better knowledge on the subject, whether you like it or not.”
