Designed by demirbey05

For the past 6 years, I have been driven by an obsession with self-learning. In the internet age, access to information has been democratized; the ability to learn independently is now the most critical skill one can possess. Traditional university curricula often lag behind the State of the Art (SOTA). I believe universities should primarily serve as hubs for networking and research, while the actual transfer of knowledge should be led by those with a true gift for teaching.

Motivated by this vision, I have prepared this AI Master’s Curriculum. This is a rigorous, 2-year journey designed for those who want to master the field from first principles to SOTA.

🛠 Prerequisites

Before starting this curriculum, you should have a solid foundation in the following:

• Mathematics: Calculus, Linear Algebra, and Probability Theory.
• CS Fundamentals: Algorithms and Data Structures.
• Programming: Mastery of the basics (you can leverage AI for boilerplate, but you must understand the logic).

📅 Year 1: Core Mastery

First Semester: Fundamentals

• [Stanford CS231n] Deep Learning for Computer Vision Course Website | The definitive guide to CNNs and visual recognition.
• [Stanford CS224n] NLP with Deep Learning Course Website | Covers everything from Word2Vec to Transformers.
• [Stanford CS229] Machine Learning Course Website | The bedrock of ML theory and practice.

Second Semester: Advanced Foundations

• [MIT 6.7960] Deep Learning Course Website | Advanced architectures and modern DL theory.
• [Stanford EE364A] Convex Optimization YouTube Playlist | Understanding the engine behind how models actually "learn."
• Information Theory YouTube Playlist | Information Theory, Inference, and Learning Algorithms.

📅 Year 2: Specialization & Research

The second year is dedicated to hands-on project work and deep-diving into specific domains.

Current Specialization Tracks:

• Generative AI: Focus on Diffusion models, LLM fine-tuning, and RAG architectures.
• AI for Healthcare: Medical imaging, genomics, and clinical decision support.
• [More specializations to be updated...]

"Education is what remains after one has forgotten what one has learned in school." — Albert Einstein (Adapted)