Machine Learning Computer Science

Rok Seo

Davis, CA *******@*****.*** 808-***-**** LinkedIn

EDUCATION

University of California, Davis Davis, CA

Bachelor of Science in Computer Science

● Relevant Coursework: Data Structures and Algorithms, Theory of Computation, Artificial Intelligence, Machine Learning, Database Management(SQL), Agent Based Modeling EXPERIENCE

Davis Neural Network Association

Content Creation Chair

February 2024 - Present

● Spearheaded the development of a new club website aimed at enhancing online presence and member engagement.

● Assembled and led a team of 13 members made up of developers, designers, and project managers to foster a collaborative environment and encourage communication.

● Utilized Git for version control, setting up a GitHub repository to streamline code contributions, reviews and updates, enhancing team productivity and collaboration. Sigma Chi Davis

Website Development and Management

February 2023 - February 2024

● Redesigned, developed, and maintained a dynamic website for Sigma Chi, enhancing the online presence and communication between members.

● Utilized HTML, CSS, and JavaScript for front-end development to structure and style the user interface in order to create intuitive user interactions and a visually appealing design.

● Implemented features such as sign ups for events, contact information, and links to the mission and goal.

● Regularly updated to maintain website content, ensuring accuracy and relevance. Projects

Fraud Prediction with Machine Learning

● Conducted a study on detecting fraudulent vehicle insurance claims using various machine learning techniques.

● Implemented and evaluated multiple machine learning models, including Logistic Regression, Naive Bayes, Decision Tree, Random Forest, and Neural Networks using Python and Scikit-learn.

● Visualized and interpreted model results using Matplotlib and Seaborn to communicate findings effectively.

● Achieved a maximum accuracy of 95% with a Neural Network model

● Built a GUI for the project using Flask, enabling users to input data and receive real-time predictions. Deep Q Learning for Atari Pong

● Implemented and trained a Deep Q Learning (DQN) model for an AI game agent using Python and PyTorch.

● Utilized reinforcement learning principles to train the model, incorporating convolutional neural networks to process game frames and make decisions.

● Demonstrated problem-solving abilities in overcoming challenges related to model convergence and computational efficiency during training.

● Tuned hyperparameter tuning and optimized network architecture to enhance learning efficiency and performance, with the model reaching a performance level competitive with human players. SKILLS

Languages: C, Java, Python, JavaScript, HTML/CSS, SQL Frameworks & Tools: React, PyTorch, Tensorflow, NumPy, Scikit-learn, Selenium Technologies: Docker, Git, Jupyter Notebooks/ Google Colab, GitHub

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