I have recently graduated with Master of Artificial Intelligence [MAI] from Memorial University of Newfoundland, NL, Canada, achieving a perfect cumulative GPA of 4.0. My formal convocation ceremony is scheduled for May 28, 2024
The Master of Artificial Intelligence program (MAI) is a two-year interdisciplinary course, combining AI topics from Computer Science, Data Science and Computer Engineering jointly offered by the Departments of Computer Science and Electrical and Computer Engineering of Memorial University.
MAI PROGRAM STRUCTURE – WITH ELECTIVES CHOSEN
| FALL 2022 | |
| AI Foundations | Foundations of Linear Algebra and Vector Spaces |
| Differential Calculus and Linearization | |
| Probability Distributions and Statistical Analysis | |
| Optimizations (Grad Descent, Convex Opt, etc.) and PCA | |
| Applied Algorithms | Graph & Tree Search: Dijkstra’s, Bellman-Ford etc. |
| Dynamic Programming and Optimization | |
| Network Flows: Ford Fulkerson, Min Flow/Max Cut | |
| Complexity Theory and Advanced Topics | |
| Software Foundations | Data Structures and Algorithms |
| Advanced Data Structures | |
| Exception Handling and File Operations | |
| GUI Design and Event-Driven Programming | |
| WINTER 2023 | |
| Topics in AI | NLP Basics: FSA, FF-NN, R-NN, Transformers |
| Robotics: Direct & Inverse Kinematics, Perception | |
| Human AI Collaboration: Teleoperation | |
| Swarm Robotics: Synchronization, Collective Operation | |
| Machine Learning | Classification and Regression |
| Model Assessment, Selection, and Evaluation | |
| Random Forests, Bagging and Boosting | |
| Support Vector Machines, Convolutional-NN | |
| ML-Project: Text to Image Search using CLIP | |
| SPRING 2023 | |
| Industrial Machine Vision | Image Enhancement and Analysis Techniques |
| Morphological Processing, Image Segmentation | |
| Pattern Recognition & Applications | |
| Vision Project: Dimensional Analysis for Quality Control | |
| Software Design & Specs | development life cycles and methodologies |
| UML notations and diagrams | |
| Requirements Elicitation & Design Analysis | |
| Deployment and Testing strategies | |
| Sofware Design Project | |
| FALL 2023 | |
| AI-CAPSTONE | Visual Question Answering (VQA) Chat application |
| VQA 2.0 Dataset: Annotations, Questions, Images | |
| Models: ViLT, LSTM & VGG19 Based Attention Model | |
| UI: Chat UI, User accounts, Model Accuracy Display | |
| Deployment: AWS-EC2 [Python-Flask-Gunicorn-NGinx] | |
| Data Analysis | |
| Using R and Python | Data Manipulation: tidyverse – R & Pandas – Python |
| Statistical Modelling: Monte Carlo simulation | |
| Time series, Temporal data processing & forecasting | |
| Visualization: ggplot2 – R, matplotlib & seaborn- Python | |
| WINTER 2024 | |
| Algorithmic Techniques | Search Algorithms: BFS, DFS, IDDFS, A* |
| for AI | Game Theory: Minimax and Alpha-Beta pruning |
| Evolutionary Algorithms and Genetic Algorithms | |
| Reinforcement Learning: SARSA, Q-Learning | |
| Neural Networks and Introduction to Deep Learning |
Thevananthan Thevarasa 