Session II: Classification & Natural Language Processing

Topics

• Classification Problems

  • k-Nearest Neighbors

  • Support Vector Machines

• Text-based data: Natural Language Processing

PDF Notes

Session II - Classification_NLP

Jupyter Notebooks

session2_fashionMNIST.ipynb
session2_NLP.ipynb
SupportVectorMachines.ipynb svm.png

References

Logistic Regression

• Chapter 4.3 of Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. “An Introduction to Statistical Learning”, Springer 2021.
• Chapter 7 of Sergios Theodoridis, “Machine Learning - A Bayesian and Optimization Perspective”, Academic Press, 2015.
• Chapter 10 of Kevin P. Murphy, “Probabilistic Machine Learning: An introduction”, MIT Press, 2021 (PDF available).

Support Vector Machines

• Chapter 9 of Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. “An Introduction to Statistical Learning”,Springer 2021.
• Sections 12.1-12.2 and 12.4 of Marc P. Deisenroth and A. Aldo Faisal and Cheng Soon Ong, “Mathematics for Machine Learning”, Cambridge University Press, 2020 (PDF available).

K-Nearest Neighbors

• Chapter 13 of Trevor Hastie, Robert Tibshirani, and Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Springer, 2009 (PDF available).
• Chapter 16 of Kevin P. Murphy, “Probabilistic Machine Learning: An introduction”, MIT Press, 2021 (PDF available).

Natural Language Processing

• Chapter 17 of Shai Shalev-Shwartz and Shai Ben-David. “Understanding machine learning: From theory to algorithms”, Cambridge University Press, 2014 (PDF available).
• Chapter 7 of Müller, Andreas C. and Sarah Guido. “Introduction to machine learning with Python: a guide for data scientists”, O’Reilly, 2016.