AI/ML Roadmap: Targeting Doctoral-Level Expertise in CV & RL

Vision Statement

My objective is to achieve Doctoral-level expertise in specialized Artificial Intelligence domains, leveraging my Electrical and Electronics Engineering foundation. The focus is on Computer Vision (CV) and Reinforcement Learning (RL), applied to complex dynamic and spatial systems. My progress will be measured through production-grade projects and peer-reviewed quality technical deep dives.

Phase I: Foundational Mastery

Goal: Solidify core ML principles and advanced mathematics essential for Deep Learning.

Focus Area	Core Concepts & Disciplines	Key Output/Deliverable
Applied Mathematics	Linear Algebra for DL, Multivariable Calculus (Jacobian, Hessian), Optimization Theory.	[Deep Dive]: The Calculus of Loss Functions and Backpropagation.
Core ML & Statistics	Supervised/Unsupervised (Regression, SVM, Clustering), Probability (Bayesian Methods).	[Project]: Statistical Model for E-E Time Series Data (e.g., Load Forecasting).
Python & Libraries	Python OOP & Efficiency, NumPy/Pandas, Git/GitHub Mastery.	Fully version-controlled project repository, demonstrated clean code.

Phase II: Deep Learning Specialization (The Core)

Goal: Master the architectures and frameworks (PyTorch/TensorFlow) required for the target fields (CV/RL).

🛠️ Sub-Focus A: Computer Vision (CV) Foundation

Focus Area	Core CV Architectures & Techniques	Key Output/Deliverable
CNN Mastery	Convolutional Layers, Pooling, Transfer Learning (ResNet, VGG).	[Deep Dive]: Analyzing Weight Initialization Schemes in CNNs.
Advanced Vision	Object Detection (YOLO/R-CNN), Semantic Segmentation (U-Net).	[Project]: Custom Object Detector using a modern framework (e.g., PyTorch Lightning).
Vision Transformers	ViT Architecture, Attention Mechanism (Self-Attention).	[Project]: Implement Vision Transformer (ViT) for image classification.

🤖 Sub-Focus B: Reinforcement Learning (RL) Foundation

Focus Area	Core RL Algorithms & Theory	Key Output/Deliverable
Value-Based RL	Markov Decision Processes (MDPs), Dynamic Programming, Q-Learning, DQN.	[Project]: DQN Agent for a Classic Control Task (e.g., CartPole).
Policy-Based RL	Policy Gradients (REINFORCE), Actor-Critic Methods (A2C/A3C).	[Deep Dive]: The Policy Gradient Theorem and its Derivation.

Phase III: Doctoral-Level Synthesis & Application

Goal: Combine CV and RL expertise to tackle complex, novel problems and produce research-quality work.

Focus Area	High-Level Application & Research Focus	Key Output/Deliverable
RL + Vision	Using vision as input for RL agents (e.g., training an agent to play a game from screen input).	[Project]: Deep RL Agent capable of navigating a simulated environment using pixel data.
Advanced DL	Generative Models (GANs/Diffusion Models), Model Interpretability (XAI).	[Deep Dive/Manuscript]: Comparison of Explanatory Methods (LIME/SHAP) on a CV model.
Research Synthesis	Identifying a novel research gap in the intersection of E-E, CV, and RL (e.g., using RL for optimizing vision processing in embedded systems).	High-Quality Manuscript (Pre-Print) submitted to the ‘Pre-Prints / Working Papers’ section.