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Projects and homework from my CS 7333 Machine Learning course
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CS 7333: Machine Learning

This repository contains all of my projects and the corresponding reports from my Machine Learning course at the University of Tulsa. What follows is a remarkably brief description of each project; for a more in-depth description (and analyses), check the report subfolder of each project.

Project 1: Unsupervised Clustering

This first project consisted of investigating unsupervised clustering, using two algorithms in particular: EM for a Mixture of Guassians and k-means. These two algorithms are iterative. EM assigns soft probabilities to the points that it's clustering, and k-means assigns a single label to each point.

I implemented k-means in Python and EM in C++, using the matrix library armadillo. EM performed much better than k-means on our datasets.

This project was incredibly frustrating for me---despite the simplicity of the algorithms, I ran into countless numerical stability issues. This project taught me how cool unsupervised learning and logarithmic math are.

Project 2: Support Vector Machines

The second project had us implement the SMO algorithm for Support Vector Machines and explore the results on six datasets, five of our own choosing and the typical two spirals dataset, using a Gaussian kernel and a polynomial kernel. I chose a quintic polynomial for my kernel.

The Gaussian kernel significantly outperformed the quintic kernel on every dataset. SVMs are a beautifully designed mechanism, and I highly recommend the Stanford CS229 lecture notes explaining them.

My most important takeaway from this project was how effective SVMs were for classifying datasets; all that you have to do is change the kernel, and they learn to classify a different distribution of data. Unfortunately, my algorithm was rather slow, but that may have been the naïve way that SMO was implemented.

Project 3: Naive Bayes

This project was the simple implementation of the Naive Bayes classifier. I won't go into much detail on this project (because it was a small project and the algorithm is very commonplace), but it was fun.

Project 4: Reinforcement Learning

The fourth project had us implement the Q-learning and SARSA algorithms (and their corresponding lambda versions) on the Tic-Tac-Toe domain.

Fascinating about this project was that we tested our algorithm against a minimax player (which I had to implement in my first AI course!)---that player was slow, even with such a small game tree. Our reinforcement player learned quickly, and played optimally even more quickly, a fascinating advantage that comes from memorizing the correct move in each possible state.

Project 5: Deep Learning

For this project, I wanted to investigate a variety of deep learning techniques to gain a better understanding of when these methods can be applied, as opposed to a more in-depth exploration of a single technique. Mostly I followed TensorFlow tutorials and used others' code; I messed with some parameters and tried to understand the various algorithms.

I ran a convolutional neural network, a recurrent neural network, an autoencoder, a wide and deep linear combined classifier, and the word2vec algorithm. See the report for more explanations and details.