The purpose of this project is to help provide some initial feel for risk, probability and "betting." Purchasing a stock is, after all, a bet that the stock will increase in value.
A navigation mesh is a means to automatically identify points at which to place path nodes.
In this project, I use what I learned about optimizers to optimize a portfolio. I will find how much of a portfolio's funds should be allocated to each stock so as to optimize its performance.
One use of AI in games is to perform path planning, the search for a sequence of movements through the virtual environment that gets an agent from 1 location to another w/o running into obstacles.
A path network is a set of path nodes and edges that facilitates obstacle avoidance.
A multilayer perceptron is a class of feedforward artificial neural network.
The objective of this project is to implement and test path planning capabilities for Cozmo, specifically the RRT algorithm.
In this project, my team and I in our Computing for Data Analytics (Machine Learning) course developed an algorithm that generates unbiased United States voting districts.
In this project, the robot will be tasked with automating a warehouse. It will have to collect cubes from the pickup zone & deliver them to the storage zone avoiding the fragile obstacles in the way.
Humor meets computer vision. This is a prototype of a device to wake up lazy bones.
An enormous problem within retail stores is the friction in the process of customer service.
I use Principal Component Analysis (PCA) to perform image compression, trying to find the most important pixels to keep while incrementally removing details.
In this project, I am writing a Finite State Machine.
In this project, I will implement two algorithms for clustering, namely the KMeans and Gaussian Mixture Model (GMM).
The objective of this project is to use image processing and machine learning tools to correctly classify images.
The objective of this project is to implement a Particle Filter (a.k.a. Monte Carlo Localization).
Created a Pacman agent with various algorithms including graph search traversal, Markov decision processes, and particle filtering.
In this project, I will be implementing neural nets, and in particular the most common algorithm for learning the correct weights for a neural net from examples.
Pacman spends his life running from ghosts, but things were not always so. Legend has it that many years ago, Pacman's great grandfather Grandpac learned to hunt ghosts for sport.
The Challenge: Using game play-by-play score sheet data from NCAAs website, build an opponent scouting report.
In this project, my Pacman agent will find paths through his maze world, both to reach a particular location and to collect food efficiently.
In this project, I will implement value iteration and Q-learning. I will test my agents first on Gridworld, then apply them to a simulated robot controller (Crawler) and Pacman.
The project uses a basic neural network to predict bike-sharing data on any given day given a history of previous bike-sharing data.
I was assigned to work on the perception of a robot adapted for repeating tasks based on previous examples (e.g. making a cup of coffee) with the aid of a human.