Session II: Classification & Natural Language Processing

Topics

• Classification Problems
  • Logistic Regression
  • k-Nearest Neighbors

Workbook about Iris dataset (will not be covered in detail in class): iris_classification.ipynb, iris_classification.py

• Text-based data: Natural Language Processing

Workbook about classification of e-mails: newsgroups_class.ipynb, newsgroups_class.py
Workbook about image data: workshop_classification.ipynb, workshop_classification.py Workbook about image data: workshop_classification_sol.ipynb, workshop_classification_sol.py

Notes of session

• PDF

Recording of session

• Zoom Recording (Session II)

References

Logistic Regression

• Chapter 3 of James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. “An Introduction to Statistical Learning”, Vol. 112. Springer, 2013 (PDF available)
• 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).

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.