This repository contains programming assignments from the various computer science courses I've completed during my time at Washington University in St. Louis. Its purpose is three-fold. First, it provides me an opportunity to keep my code under source code control for future reference. Second, it gives a demonstrable overview of my technical skills. Finally, it allows for a perspective on how my skills have progressed over time.

• Computer Science I (CSE 131)

• Object Oriented Software Development Laboratory (CSE 332S) (Code not available)

• Advanced Algorithms (CSE 441T) (Code not available)

• Introduction to Artifical Intelligence (CSE 511A)

• Data Mining (CSE 514A) (Code not available)

• Machine Learning (CSE 517A)

Course description: Introductory course in computer science. Covered basic programming techniques, loops, recursion, basic data structures, polymorphism, and object-oriented programming.

Professor: [Ron K. Cytron] (http://www.cs.wustl.edu/~cytron/)

Language Used: Java

Final Course Grade: A+

Taken Spring 2015

Course description: Intensive focus on practical aspects of designing, implementing and debugging object-oriented software. Special focus on design and implementation based on frameworks, as they are central themes enabling the construction of reusable, extensible, efficient, and maintainable software.

Professors: Dr. Christoper Gill, Dr. Ruth Miller

Course Site: http://classes.cec.wustl.edu/~cse332/

Language(s) Used: C++

Final Course Grade: B

Taken Spring 2014

Course description: An advanced course on the topic of algorithms and data sturctures. The majority of the course introduces and analysis some famous algorithms like Greedy and Dynamic Programming.

Professors: Robert Pless

Course Site: http://www.cs.wustl.edu/~pless/441/

Language(s) Used: N/A

Final Course Grade: A-

Taken Fall 2015

Course description: An advanced course on the topic of Machine Learning. The majority of the course is a thorough overview of Supervised Learning, including popular algorithms like decision trees and variants, nearest neighbors, ANNs, etc, but also theoretical implications of supervised learning approaches such as the bias-variance decomposition and the bias-variance tradeoff, cross-validation, etc. The end of the course was a survey of dimensionality reduction methods, comparison of machine learning approaches (e.g. frequentist vs. bayesian, etc), gaussian processes and bayesian optimization, and generative mixture models and applications, like clustering.

Professors: Roman Garnett

Course Site: http://www.cse.wustl.edu/~garnett/cse511a/

Language(s) Used: Python

Final Course Grade: A-

Taken Fall 2015

Course description: An advanced course on the topic of Machine Learning. The majority of the course is a thorough overview of Supervised Learning, including popular algorithms like decision trees and variants, nearest neighbors, ANNs, etc, but also theoretical implications of supervised learning approaches such as the bias-variance decomposition and the bias-variance tradeoff, cross-validation, etc. The end of the course was a survey of dimensionality reduction methods, comparison of machine learning approaches (e.g. frequentist vs. bayesian, etc), gaussian processes and bayesian optimization, and generative mixture models and applications, like clustering.

Professors: Dr. Marion Neumann

Course Site: http://sites.wustl.edu/neumann/courses/spring-2016/cse-517/

Language(s) Used: Matlab

Final Course Grade: A-

Taken Spring 2016