My personal study notes for ML, prob, stats, etc. It helps me to review something old and learn something new 🌟
- VI: Variational Inference
- PCA: Principle Component Analysis
- EM: Expectation-Maximization Algorithm
- tSNE: t-distributed Stochastic Neighbor Embedding
- UMAP: Uniform Manifold Approximation and Projection
- MRF: Markov Random Field
- CRF: Conditional Random Field
- HMM: Hidden Markov Model