Resources I found that are good reference for learning various topics.
Gaussian Process
- It is useful to first review the properties of Gaussian distribution. Pattern Recognition and Machine Learning, chapter 2.3 , Christopher Bishop.
- [ video ] Introduction to Gaussian Process regression, Course taught in 2013 at UBC by Nando de Freitas.
- Quick, short and simple introduction of GP. A Tutorial on Bayesian Optimization, section 3, Peter I. Frazier.
- Get a better understanding of kernel and GP. Pattern Recognition and Machine Learning, chapter 6, Christopher Bishop.
- To know GP in details, read the book. Gaussian Processes for Machine Learning, Carl Edward Rasmussen, Christopher K. I. Williams.
- [ toolkit ] A Python library for GP. George (doc).
Dimensionality Reduction
- Multidimensional Scaling
- [ slides ] Multidimensional Scaling, Sungkyu Jung, University of Pittsburgh, 2013.
- A comprehensive tutorial. Mutidimensional Scaling.
- Locally Linear Embedding
- An Introduction to Locally Linear Embedding, Lawrence K. Saul.
Neural Architecture Search
- Neural Architecture Search: A Survey, Thomas Elsken, Jan Hendrik Metzen and Frank Hutter, 2018.
Semi-Supervised Learning
- The book. Semi-Supervised Learning, Bernhard Schölkopf, Alexander Zien.
- Semi-Supervised Learning with Graph
- [ slides ] A quick introduction. Label Propagation on Graphs, Leonid E. Zhukov, 2017.
- Semi-Supervised Learning with Graph, Xiaojin Zhu, 2005.
Graphical Models
- The book. Probabilistic Graphical Models, Daphne Koller, Nir Friedman.