Course materials, notes, and implementations from the Supervised Learning course. Covers the foundations and modern practice of supervised learning, from classical algorithms to deep learning, object detection, and Transformers.
Supervised Learning Foundations
- Formulation of the learning process
- Classification and regression frameworks
- Experiment design: dataset splits, metrics, augmentation
- Model evaluation and comparison
Classical Algorithms
- LDA (Linear Discriminant Analysis)
- Decision Trees
- k-Nearest Neighbors (k-NN)
- Support Vector Machines (SVM)
- Neural Networks
Ensemble Methods
- Bagging and Boosting
- Random Tree Ensembles
- Stacking
Classical Computer Vision
- Local descriptors: SIFT and Bag of Words (BoW)
- Viola–Jones Object Detection Framework
Deep Learning
- Convolutional Neural Networks (CNNs) — convolution, training, famous architectures, transfer learning
- Recurrent and Recursive Networks (RNNs, LSTM, GRU)
- Transformers
- Self-supervised learning
Modern Object Detection
- Two-stage: R-CNN, Fast R-CNN, Faster R-CNN
- One-stage: YOLO