Intro to Neural Networks & Machine Learning
PHYSICS 3G03
I learned about the fundamental concepts and techniques of machine learning. This included supervised and unsupervised learning, as well as the concepts of overfitting and regularization. I studied the different optimization algorithms used to train neural networks, such as gradient descent and its variants. I also learned about the different types of data preprocessing and feature engineering techniques to prepare data for neural networks. Overall, this course provided me with a solid foundation in the field of machine learning and neural networks, and a good understanding of how to apply these techniques to real-world problems.
Sample Project:
Computational
Mechanics
This course help me understand topics such as elasticity, bending moments, shear forces, and flexure on different objects. This was done using the complex algebra system Maple and the finite element method solver FlexPDE.
ENGPHYS 2P04
Sample images of sketches and scenarios used to understand concepts in statics.
Design Project Sample Write-Up
Sample images of circuits built from analog section
Analog & Digital
Circuits
In the analog section of the course I learned DC circuit analysis, AC circuit analysis using phasors, filters, and power correction factor. The digital section introduced me to logic design and implementation using logic gates. This was done using the complex algebra system Maple and the circuit simulator Multisim. I also gained practical skills when creating the physical builds of the circuits.
ENGPHYS 2E04
Sample Write-Up H5:
AC Power
Scientific Computing
During this course I became familiar with the Linux command kernel while also improving my coding skillset in c/c++. This was delivered through various math concepts that can be applied to real world through physics.
PHYSICS 2G03
Sample Write-Up:
Final Project
Computational Mechanics Write-Ups
Click to Download
**Note video links may not work**