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 |
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Sequence Models and Recurrent Neural Networks | CS/Yale | 2022/04/10 | NLP | |
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Reinforcement Learning | CS/Yale | 2022/04/01 | AI | |
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Sparsity and Graphs | CS/Yale | 2022/03/28 | Graph Theory | |
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Variational Inference | CS/Yale | 2022/03/11 | ML | |
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Approximation Inference | CS/Yale | 2022/03/07 | Statistics | |
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Gibbs Sampling | CS/Yale | 2022/03/02 | Statistics | |
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Gibbs Sampling | CS/Yale | 2022/03/02 | Statistics | |
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Dirichle Processes | CS/Yale | 2022/02/19 | ML | |
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Neural Tangent Kernels | CS/Yale | 2022/02/19 | ML | |
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Representer Theorem | CS/Yale | 2022/02/10 | Statistics | |
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Mercer's Theorem | CS/Yale | 2022/01/16 | Statistics | |
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Sparsity Meets Convexity | CS/Yale | 2022/01/16 | Statistics | |
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Expectation Maximization | CS/Yale | 2022/12/06 | Statistics | |
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How to Manage Large Experiments with Brain Imaging Datasets | CS/Yale | 2022/12/05 | Software Engineering | |
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Mixture Models and EM | CS/Yale | 2022/12/03 | ML | |
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Posterior Inference | CS/Yale | 2022/11/26 | ML | |
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Empirical Risk Minimization | CS/Yale | 2022/11/16 | ML | |
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Maximum Likelihood and Maximum A Posteriori Estimation | CS/Yale | 2022/11/16 | ML | |
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Stochastic Functions in Python | CS/Yale | 2022/11/01 | ML | |
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Kernel Methods | CS/Yale | 2022/10/20 | ML | |
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