📚 References
🧠 Algorithms
Stanford Algorithms
75 videos • ~15 min each • Stanford
- Foundations: Integer Multiplication, Karatsuba
- Divide & Conquer: Merge Sort, Closest Pair
- Asymptotic Analysis: Big-O, Omega, Theta
- Master Theorem (full proof)
- Sorting: Quicksort, pivot strategies
- Selection: Randomized & Deterministic
- Graphs: Min Cut, BFS, DFS, SCC
- Shortest Paths: Dijkstra
- Data Structures: Heaps, BST, Red-Black Trees
- Hashing + Bloom Filters
MIT 6.006 – Introduction to Algorithms
32 videos • ~1 hr each • MIT
- Algorithmic Thinking & Complexity
- Data Structures: Arrays, Sets
- Sorting & Hashing
- Trees: Binary, AVL
- Heaps
- Graphs: BFS, DFS
- Shortest Paths: Dijkstra, Bellman-Ford
- Dynamic Programming (LCS, LIS, Subset Sum)
- APSP, Johnson’s Algorithm
- Complexity Theory
🤖 Artificial Intelligence
Stanford CS221 – AI Foundations
20 videos • ~1h 15m each • Stanford
- Search (I & II)
- MDP & Reinforcement Learning
- Policy Gradient
- Game Playing
- Bayesian Networks (Inference + Learning)
- Logic (I & II)
- Language Models
- AI & Society
Stanford CS230 – Deep Learning
9 videos • ~1.5 hr each • Stanford
- Deep Learning Introduction
- Supervised, Self-Supervised, Weakly Supervised
- Full DL Project Lifecycle
- Generative Models & Adversarial Robustness
- Deep Reinforcement Learning
- AI Project Strategy
- Agents, Prompting, RAG
- Model Interpretability
- Career Guidance
MIT 6.S087 – Foundation Models & Generative AI
9 videos • ~40 min each • MIT
- Neural Networks & Supervised Learning
- Unsupervised & Representation Learning
- Reinforcement Learning
- Transformers & LLMs (ChatGPT)
- GANs
- Diffusion Models (Stable Diffusion, DALL·E)
- Autoencoders
- Contrastive & Self-Supervised Learning
- Foundation Models
💡 Tip: Start with Algorithms (Stanford + MIT 6.006) before deep diving into AI.
⚠️ These are full university courses — consistency matters more than speed.