dominic-sagers
Aspiring Data Scientist currently studying at Maastricht University.
Maastricht, The Netherlands
Pinned Repositories
3D-Knapsack-Problem-With-Pentominos
This project seeks to explore the famed knapsack problem in a 3D context using "parcels" shaped as pentominoes (shapes only one size unit larger than those of the famed tetrominoes from tetris) holding different values to maximise value while filling a theoretical cargo space with various algorithms.
AI-Agents-In-Imperial
This projects seeks to explore the performance and dynamics of agents playing in a full Java implemetation of the game Imperial (http://bit.ly/Imperial-wiki). An array of different agent architectures are used ranging from simple rule-based to MCTS-Deep-Neural-Network agents.
Anytime-Sequential-Halving
This repository contains the material used to complete my Thesis for my BSc in Data Science and AI. My thesis attempts to explore the work started by previous alumni at my faculty which covered an algorithm variant of MCTS implementing a technique known as Sequential Halving, and I formulate an algorithm version not requiring constraints.
Computer-Security-Project
A secure client-server system was implemented within a two-week timeframe, following the specified software architecture and implementation details. Ensured clients can register securely, perform designated actions, and maintain accurate counter values on the server while adhering to logging requirements. Read more in project readme.
Crazy-Putting-
This project implements a mini golf putting game and attempts to approximate (without accounting for rolling or flying) the real life phyiscs of golf. This program allows the player to play against several AI-bot agents utilizing different algorithms such as a modified Hill Climbing method, a Newton-Raphson Bot, and a proprietary Manhattan Bot.
Exploring-Basics-of-Machine-Learning
This repository contains many jupyter notebooks and output printouts chronicling my learning of basic Machine Learning concepts.
MovieLens-20M-Recommender-System
Using the MovieLens 20 Million review dataset, this project aims to explore different ways to design, evaluate, and explain recommender systems algorithms. Different item-based and user-based recommender systems are showcased as well as a hybrid algorithm using a modified page-rank algorithm.
Native-vs-Non-Native-Classification-With-BERT
This project attempts to tackle the problem of native English vs. non-native English recognition. Using a data-set containing passages written by native and non-native speakers, different classification models were tested to classify them. The BERT model provided the highest performance, and was further optimized.
Personal-AI-Assistant-in-Java
This project aims to create a rudementary digital assistant/chat-bot in Java which uses a Context Free Grammar (https://bit.ly/cfg-wiki) architecture allowing the user to perform and customize basic actions using a template system.
Pizza-Reastraunt-SQL-Database
In an exercise done to learn the basics of sql, this project attempts to use a rudementary commandline interface to interact and extract information from an SQL database modelled off of a pizza restaurant.
dominic-sagers's Repositories
dominic-sagers/AI-Agents-In-Imperial
This projects seeks to explore the performance and dynamics of agents playing in a full Java implemetation of the game Imperial (http://bit.ly/Imperial-wiki). An array of different agent architectures are used ranging from simple rule-based to MCTS-Deep-Neural-Network agents.
dominic-sagers/Native-vs-Non-Native-Classification-With-BERT
This project attempts to tackle the problem of native English vs. non-native English recognition. Using a data-set containing passages written by native and non-native speakers, different classification models were tested to classify them. The BERT model provided the highest performance, and was further optimized.
dominic-sagers/3D-Knapsack-Problem-With-Pentominos
This project seeks to explore the famed knapsack problem in a 3D context using "parcels" shaped as pentominoes (shapes only one size unit larger than those of the famed tetrominoes from tetris) holding different values to maximise value while filling a theoretical cargo space with various algorithms.
dominic-sagers/Computer-Security-Project
A secure client-server system was implemented within a two-week timeframe, following the specified software architecture and implementation details. Ensured clients can register securely, perform designated actions, and maintain accurate counter values on the server while adhering to logging requirements. Read more in project readme.
dominic-sagers/Crazy-Putting-
This project implements a mini golf putting game and attempts to approximate (without accounting for rolling or flying) the real life phyiscs of golf. This program allows the player to play against several AI-bot agents utilizing different algorithms such as a modified Hill Climbing method, a Newton-Raphson Bot, and a proprietary Manhattan Bot.
dominic-sagers/Exploring-Basics-of-Machine-Learning
This repository contains many jupyter notebooks and output printouts chronicling my learning of basic Machine Learning concepts.
dominic-sagers/MovieLens-20M-Recommender-System
Using the MovieLens 20 Million review dataset, this project aims to explore different ways to design, evaluate, and explain recommender systems algorithms. Different item-based and user-based recommender systems are showcased as well as a hybrid algorithm using a modified page-rank algorithm.
dominic-sagers/Personal-AI-Assistant-in-Java
This project aims to create a rudementary digital assistant/chat-bot in Java which uses a Context Free Grammar (https://bit.ly/cfg-wiki) architecture allowing the user to perform and customize basic actions using a template system.
dominic-sagers/Pizza-Reastraunt-SQL-Database
In an exercise done to learn the basics of sql, this project attempts to use a rudementary commandline interface to interact and extract information from an SQL database modelled off of a pizza restaurant.
dominic-sagers/Sequential-Halving-With-Time-Constraints-In-Ludii
This repository contains the material used to complete my Thesis for my BSc in Data Science and AI. My thesis attempts to explore the work started by previous alumni at my faculty which covered an algorithm variant of MCTS implementing a technique known as Sequential Halving, and I will attempt an algorithm using time constraints