AI/ML
Machine Learning 101
- Học máy là gì
- Linear Models: Linear Regression, Logistic Regression, Soft-max Regression
- Gradient Descent
- Generative Machine Learning: Gaussian Discriminant Analysis, Naive Bayes
- Clustering
- Kernel Method & Support Vector Machine
- Deep Learning
- Generalization
- Regularization
- Dimension Reduction: PCA, t-SNE, UMAP
- Self-supervised Learning
- Reinforcement Learning
Deep Learning 101
- Neural Networks
- Convolutional Neural Networks
- Auto-regressive Neural Networks
- Attention Mechanism
- Transformers
- Advanced Optimization Algorithm
- Deep Generative Models
- Energy-based Models
Applied Machine Learning 101
Natural Language Processing:
- Learning Language Representation
- Sequence-to-Sequence Modelling
- Pre-trained Language Models, Contextualized Representation
- Non-autoregressive Generation Models
- Knowledge-based Models
Computer Vision:
- Image Classification
- Object Detection
- Image Segmentation
- Image Generation
Advanced Topics
- Diffusion Models
- Normalizing Flows
- Optimal Transport
- Knowledge Distillation
- Bayesian Machine Learning
- Representation Learning
- Disentangled Representation Learning
- Contrastive Learning
- Gradient-free Optimization
- Evolution Algorithm
- Time-series Analysis
- Explainable AI
- Causal Learning