Interactive session, Thursday March 14
Weekly lecture:
Video Recordings:
- Overview
- scikit-learn applied to the 2021 mandatory 2
- Over-fitting and regularization
- Regularization in scikit-learn
- Bias-variance tradeoff
- Cross-validation
- Ensemble learning and Random forests
Readings:
Hal Daumé III, A course in Machine Learning
- Ch. 5 Practical issues, sec. 5.0-5.6 (p.55-66), except precision-recall curves, ROC curves and AUC curves.
Jurafsky and Martin, Speech and language processing, 3rd ed. draft, Feb 3, 2024
- Chapter 4, section 4.7 "Evaluation: Precision, Recall, F-measure", section 4.8 "Test sets and Cross-validation"
- Chapter 5, section 5.7 "Regularization"
- except the last paragraph starting with "Both L1 and L2..."
Marsland
- Chapter 2, section 2.5 (Not the formulas)
- Chapter 13: Introduction, 13.2 Bagging, 13.3 Random forest
Group sessions
There are no new weekly exercises for this week. The program this week is to work on mandatory 2.