Welcome to my website, my name is Steve Bottos. I am currently working towards a research-based Master's degree (MaSc) in Electrical and Computer Engineering, with my research dealing mainly with the realms of Machine Learning and Data Science. Here you'll find some information about myself as well as some past projects and works-in-progress. I hope that you enjoy my work as much as I enjoyed producing it. For more about me, head to the "About" section. Check out the feed below for some of my most recent work, or check out the "Projects" page.

Side Project - Analyzing All Los Angeles Parking Violations from 2018

With a bit of free time this weekend, I decided to do some hobby coding in Python, which I’ve presented here as a Jupyter Notebook. The point of this side project is to create a map of LA according to parking tickets received from last year (2018) and to produce some helpful visualizations. I want to start with the full dataset (~9-million x 19, saved as a .csv > 1gb) to gain more experience handling large .csv files that are cumbersome to work with in excel and are slow to work with in Python unless processed in batches (especially when you don’t have much RAM available).

Key Concepts: Python (Numpy/Matplotlib/Pandas/Folium(Mapping Package)), Jupyter Notebooks, Data Science, Big Data

Cognitive Context Detection With Tensorflow

In previous projects, I demonstrated how to implement a Neural Network from start to finish using only home-made functions written in both Matlab and Python. I’ve uploaded a Tensorflow implementation for the sake of comparison (note that this was written pre-Keras, Tensorflow code can now be written much more intuitively using Keras wrappers). I’ve concluded that Tensorflow offers increased productivity in the form of quicker-to-write code and faster training times. The optimization functions that TF offers are great, and allowed me to train my models in a fraction of the time that Matlab or Python+Numpy in conjunction with the GPU acceleration capabilities. Comparing the two files MatlabResults in the Matlab Implementation and TensorflowResults in this repo demonstrate the notable accuracy improvements that Tensorflow offers as well.

Key Concepts: Tensorflow, Python (Numpy/Scipy/Matplotlib/Pandas), Machine Learning, Neural Networks, Data Science

Cognitive Context Detection With Python

In a previous project, I demonstrated how to implement a Neural Network from start to finish using only home-made functions written in Matlab. I’ve finally uploaded an identical Neural Network, but written with Python. This is just the Neural Network itself for comparison, and it does not include any of the pre-processing steps that the Matlab code base showcases. Data which was already prepared is included in the repository. For a full ReadMe describing the original use of the code, and its applications, check the original Matlab repository.

Key Concepts: Python (Numpy/Scipy/Matplotlib/Pandas), Machine Learning, Neural Networks, Data Science

Hidden Markov Model Tutorial

Hidden Markov Models are powerful tools, commonly used in a wide range of applications from stock price prediction, to gene decoding, to speech recognition. This is a tutorial on Hidden Markov Models that I wrote, and thought to would make publicly available for download since I believe it captures the intuition quite well.

Key Concepts: Hidden Markov Models, Machine Learning, Tutorials, Statistical Analysis

C++ Tutorials

This repository contains a collection of C++ tutorials that I put together for a class of junior electrical engineering students. They’re designed to be short, simple, and accessible to complete beginners. I’ve been getting good feedback from the students, so I thought that I would share the tutorials with whoever may be interested in them.

Key Concepts: C++, C, Coding, Tutorials