Student presentation topics for ML course

Gaussian Process Regression

Jochen Görtler, A Visual Exploration of Gaussian Processes

Jie Wang, An Intuitive Tutorial to Gaussian Processes Regression, ArXiv

Gaussian Process Regression using Scikit-Learn

Ensemble Learning

Didrik Nielsen, Tree Boosting With XGBoost

Xibin Don, et al. A Survey on Ensemble Learning, https://doi.org/10.1007/s11704-019-8208-z

Rule-based Machine Learning

Ryan J. Urbanowicz and Jason H. Moore, Learning Classifier Systems: A Complete Introduction, Review, and Roadmap, doi:10.1155/2009/736398

Ryan Urbanowicz and Will Browne, Introduction to Learning Classifier Systems, https://doi.org/10.1007/978-3-662-55007-6

Robert Zhang, et al., A scikit-learn compatible learning classifier system

Semi-supervised learning

Xiaojin Zhu and Andrew B. Goldberg, Introduction to Semi-Supervised Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, 2009, Vol. 3, No. 1 , Pages 1-130, https://doi.org/10.2200/S00196ED1V01Y200906AIM006

Jesper E. van Engelen, Holger H. Hoos, A survey on semi-supervised learning, https://doi.org/10.1007/s10994-019-05855-6

Online Learning

András A. Benczúr, Levente Kocsis, Róbert Pálovics, Online Machine Learning in Big Data Streams

https://github.com/online-ml/river

Probabilistic Graphical Models

Daphne Koller, Nir Friedman, Lise Getoor and Ben Taskar, Graphical Models in a Nutshell

https://pgmpy.org/

Meta-learning

Joaquin Vanschoren, Meta-Learning: A Survey

Categorical Representation Learning

Artan Sheshmani, Yizhuang You, Categorical Representation Learning: Morphism is All You Need, arXiv:2103.14770

Deep Learning

Catherine F. Higham, Desmond J. Higham, Deep Learning: An Introduction for Applied Mathematicians, ArXiv

Selected applications of Machine Learning in biology

Fayou Want, et al., Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues, https://doi.org/10.3389/fgene.2020.01025

Multi-task Learning

Theodoros Evgeniou, et al., Learning Multiple Tasks with Kernel Methods, Journal of Machine Learning Research 6 (2005) 615–637

Knowledge Graph Embeddings

Federico BIANCHI et al., Knowledge Graph Embeddings and Explainable AI, ArXiv

https://dglke.dgl.ai/doc/index.html

Graph Neural Networks

Graph Neural Networks for Novice Math Fanatics

Some Mathematical Perspectives of Graph Neural Networks, Duy Nguyen, University of Waterloo thesis, 2022

Weijie Feng et al., GraphMR: Graph Neural Network for Mathematical Reasoning, ArXiv

Hierarchical Temporal Memory

D. Niu et al., A New Hierarchical Temporal Memory Algorithm Based on Activation Intensity, https://doi.org/10.1155%2F2022%2F6072316

Zero-Shot Learning

Wei Want, at al., A Survey of Zero-Shot Learning: Settings, Methods, and Applications, https://dl.acm.org/doi/10.1145/3293318