Blog Posts

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Here are my notes from teaching Yale's SD&S 365 & 565 with Professor John Lafferty and CPSC 481/581 with Andre Wibisono. Each course aimed to deepen students' understanding of machine learning. If you have ideas to enhance the content, please reach out.

Resource Highlights:

RNN Recurrent neural networks
RL Reinforcement Learning
GS Gibbs Sampling
VAE Variational Auto Encoders
LSTM Long Short-Term Memory
NLTK Neural Tangent Kernels

Title School Date Topic Info
Sequence Models and Recurrent Neural Networks CS/Yale 2022/04/10 NLP

Reinforcement Learning CS/Yale 2022/04/01 AI

Sparsity and Graphs CS/Yale 2022/03/28 Graph Theory

Variational Inference CS/Yale 2022/03/11 ML

Approximation Inference CS/Yale 2022/03/07 Statistics

Gibbs Sampling CS/Yale 2022/03/02 Statistics

Gibbs Sampling CS/Yale 2022/03/02 Statistics

Dirichle Processes CS/Yale 2022/02/19 ML

Neural Tangent Kernels CS/Yale 2022/02/19 ML

Representer Theorem CS/Yale 2022/02/10 Statistics

Mercer's Theorem CS/Yale 2022/01/16 Statistics

Sparsity Meets Convexity CS/Yale 2022/01/16 Statistics

Expectation Maximization CS/Yale 2022/12/06 Statistics

How to Manage Large Experiments with Brain Imaging Datasets CS/Yale 2022/12/05 Software Engineering

Mixture Models and EM CS/Yale 2022/12/03 ML

Posterior Inference CS/Yale 2022/11/26 ML

Empirical Risk Minimization CS/Yale 2022/11/16 ML

Maximum Likelihood and Maximum A Posteriori Estimation CS/Yale 2022/11/16 ML

Stochastic Functions in Python CS/Yale 2022/11/01 ML

Kernel Methods CS/Yale 2022/10/20 ML