📘 aIDoctorate — Syllabus & Progress
This page is my living syllabus and progress tracker for the aIDoctorate in Computational Science & AI. The structure and workload are modeled after accredited PhD programs such as the Computational & Data Science PhD at MTSU, which require 72–84 credit hours.
Because I hold an MBA, I have been granted 6 credit hours of advanced standing for prior coursework in Research Methods and Ethics. This reduces my total aIDoctorate requirement to 66 credit hours, while keeping all core technical and research elements intact.
🏛 Program Requirements
Total Required: 66 credit hours (after MBA adjustment)
- Core Courses: 24 cr.
- Electives / Specializations: 6 cr.
- Seminars & Defenses: 6 cr.
- Dissertation Research: 30 cr.
- MBA Transfer Credit: 6 cr.
📚 Core Courses (24 credit hours)
Each course is equivalent to 3 credit hours.
- Mathematics for AI & Computational Science (3)
- Numerical Methods & Scientific Computing (3)
- Machine Learning Foundations (3)
- Deep Learning & Neural Networks (3)
- Data Science & Statistical Learning (3)
- High-Performance & Parallel Computing (3)
- Research Methods & Scientific Writing (waived with MBA) (3)
- Ethics in AI & Computational Science (waived with MBA) (3)
🎯 Electives / Specializations (6 credit hours)
Choose 2 electives (instead of 4, due to MBA transfer credit).
- Reinforcement Learning (3)
- Natural Language Processing & Transformers (3)
- Generative AI & Creativity (3)
- Computational Modeling & Simulation (3)
- AI for Automation & Multi-Agent Systems (3)
- AI for Media/Film or Sports Analytics (Applied Elective) (3)
🎤 Seminars & Oral Defenses (6 credit hours)
- Paper Reproduction Seminar (3) — reproduce and critique three published AI/CS papers
- Oral Qualifying Exam & Defense (3) — major milestone oral exam
📝 Dissertation Research (30 credit hours)
- Proposal Development (6)
- Dissertation Research & Writing (18)
- Final Defense & Public Portfolio Publication (6)
✅ Progress Tracker
- Blog Post – Minimum Math for Modern AI
- Project – Monte Carlo π Estimator
- Project – Gradient Descent Visualizer
- Blog Post – Why Computational Science Bridges Math and AI
- Oral Exam – Math & ML Basics
📊 Progress Notes
- I will log ~3–4 study hours per week.
- Each 3-credit course represents ~100 study hours, spread over 2–3 months.
- Milestones include GitHub repos, blog posts, and oral defense recordings.
- The journey is expected to span 3–4 years, similar to accredited PhD timelines.