Matthew M. S. Bullen

JD, MA, ALM-IMS, MS-CIA

University of Essex – MSc Artificial Intelligence ePortfolio

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This site is organized by module and unit. Use the sidebar to jump to each module.

Code Examples

Code used for assigned portfolio exercises is presented in Google Colab notebooks. Please note that much of the code was provided solely for the purpose of general discussion during seminars and did not include separate written components.

About Me

I'm a full-stack developer with over ten years of experience based in Los Angeles, California. My practice areas include cloud systems development, business process automation, and cybersecurity.

Understanding Artificial Intelligence

January 2025 A

Collected Unit Notes

Unit 1: Introduction to Artificial Intelligence

Unit 2: The Benefits and Threats to Business

Unit 3: Artificial Intelligence and Learning Systems

Unit 4: Understanding the Process of Developing a Learning System

Unit 5: Approaches to Learning System Development – Focus on Machine Learning and Deep Learning

Unit 6: Applying Machine Learning Algorithms to Develop a Learning System

Unit 7: Feature Engineering and Representation

Unit 8: Application of Supervised Learning Approaches – Classification and Regression Tasks

Unit 9: Application of Unsupervised Learning Approaches – Clustering

Unit 10: Evaluating Learning Systems

Unit 11: Application of Training Procedure to Learning Algorithms and Evaluation of Learning Systems

Unit 12: Future of AI Systems and Professionalism

Numerical Analysis

April 2025 B

Collected Unit Notes

Unit 1: Types and Sources of Data and an Introduction to R

Unit 2: Data Structures in R

Unit 3: Data Management in R

Unit 4: Probabilities, Probability Distributions, and Sampling

Unit 5: Producing Plots and Introducing Calculus

Unit 6: Statistical Inference, Confidence Intervals, and Hypothesis Testing

Unit 7: Parametric Tests

Unit 8: Nonparametric Tests

Unit 9: Creating Cross-Tabulations and Performing Chi-Square Analysis

Unit 10: Correlation

Unit 11: Regression Analysis

Unit 12: Bayesian Data Analysis

Machine Learning

July 2025 B

Collected Unit Notes

Unit 1: Introduction to Machine Learning

Unit 2: Exploratory Data Analysis

Unit 3: Correlation and Regression

Unit 4: Linear Regression With Scikit-Learn

Unit 5: Clustering

Unit 6: Clustering with Python

Unit 7: Introduction to Artificial Neural Networks

Unit 8: Training an Artificial Neural Network

Unit 9: Introduction to Convolutional Neural Networks

Unit 10: Natural Language Processing

Unit 11: Model Selection and Evaluation

Unit 12: Industry 4.0 and Machine Learning