Optional References

These references are optional.

Textbooks

• Probabilistic Machine Learning: An Introduction (2021) and Machine Learning: a Probabilistic Perspective (2012) , by Kevin Murphy

• Convex Optimization: Algorithms and Complexity (Free Version), by Sebastien Bubeck

• A Course in Machine Learning, by Hal Daume III

Papers and Tutorials

• The Matrix Cookbook (a lot of useful properties of matrices). [link]

• Stochastic Gradient Descent Tricks [link]

• Papers on Ensemble Selection. [paper1][paper2][KDD Cup Report]

• Practical Bayesian Optimization for Efficient Grid Search of Tuning Parameters. [paper][software]

• Reasonably Accessible Paper on Regularized Multi-Task Learning. [paper]

• Overview of Topic Models. [paper]

• Overview of Structural SVMs. [paper]

• A Brief Overview of Deep Learning. [link]

• Illustrated guide to RNNs [post1] and LSTMs [post2]

• Tutorial on Learning Reductions. [slides]

• Learning Reductions Overview. [paper]