Project Background
Taught by Nakul Gopalan, a postdoctoral fellow in the CORE Robotics Lab at Georgia Tech, this course introduced techniques in machine learning with an emphasis on algorithms and their application to real-world data.
We learned methods of supervised learning, including decision trees, support vector machines (SVM), and neural networks. We also learned methods of unsupervised learning, including k-means clustering and kernel density estimation, as well as dimensionality reduction techniques like principal component analysis (PCA) and linear discriminant analysis (LDA). Lastly, we learned about more advanced topics like hidden Markov models and reinforcement learning.
For more information about the specific course I took, visit the course home page.
A major component of this course was a semester-long team project, in which we were tasked with using machine learning methods to solve a real-world problem.
My team's final report and presentation adequately summarize our project, so I've embedded them below (best viewed on desktop).
 |  |  |
|---|---|---|
 |  |  |
 |  |
