CSCI 4052U
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Background
▶ Probability
Background
Background to Machine Learning
☑ Functional Modeling
☑ Linear Algebra and Calculus
▶ Probability
Neural Networks
☐ Neural Networks
☐ Fully Connected Layer
☐ Embedding Layers
☐ Convolution and Transpose Conv
☐ Residual Layers
☐ Dropout and Normalization Layers
☐ Attention and Transformers
☐ Recurrence Networks
☐ Autoencoder and Variational Autoencoder
Vision
☐ Vision
☐ Classification
☐ Region-based CNN
☐ YOLO Models
☐ Vision Transformers
☐ Contrastive Language Image Pre-training
☐ Stable Diffusion
Language
☐ Language
☐ Tokenization
☐ Language Modeling
☐ Attention Is All You Need
☐ Masked Language Modeling With BERT
☐ Neural Machine Translation Using T5
☐ Generative Pretrained Transformers
☐ Evaluating Natural Language Processing Tasks
Reinforcement Learning
☐ Reinforcement Learning
☐ Basics of Reinforcement Learning
☐ Deep Q-Learning
☐ Policy Optimization
☐ Application of RL to LLM
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Probability in Machine Learning
▶ Probability
Author
Ken Pu
1
Probability in Machine Learning