Chapter 13 References

Books

• Kevin P. Murphy, Machine Learning: A Probabilistic Perspective — supervised learning, linear models, SVMs, neural networks, clustering, and PCA.
• Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning — regularized regression, classification, ensembles, boosting, and dimensionality reduction.
• Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning — neural-network mechanics, CNNs, recurrent networks, optimization, and representation learning.
• Simon J. D. Prince, Understanding Deep Learning — attention, Transformers, normalization, and modern training methods.
• Richard Szeliski, Computer Vision: Algorithms and Applications — visual feature extraction, video processing, object detection, and tracking.

Websites

• Scikit-learn supervised learning guide — linear models, SVMs, nearest neighbors, trees, forests, and gradient boosting.
• Scikit-learn unsupervised learning guide — clustering, dimensionality reduction, density estimation, and anomaly detection.
• Scikit-learn neural-network models — multilayer perceptrons and related estimators.
• XGBoost documentation — regularized gradient-boosted trees.
• LightGBM documentation — histogram-based and leaf-wise gradient boosting.
• CatBoost documentation — ordered boosting and categorical-feature handling.
• FAISS — similarity search and clustering for dense vectors.
• Hinton and Salakhutdinov, “Reducing Dimensionality with Neural Networks” — autoencoder-based representation learning.
• Redmon et al., “You Only Look Once” (YOLO) — one-stage object detection.
• Zhang et al., “ByteTrack” — multi-object tracking with low-confidence detections.
• Dehghani et al., “DeepSORT” — multi-object tracking with motion and appearance cues.
• Feichtenhofer et al., “SlowFast Networks” — two-pathway video recognition.
• Bertasius et al., “Is Space-Time Attention All You Need for Video Understanding?” — spatiotemporal Transformer attention.
• Liu et al., “Video Swin Transformer” — hierarchical video Transformer architecture.
• Dao et al., “FlashAttention” — IO-aware exact attention.