Explores Transductive Support Vector Machine for semi-supervised clustering, aiming for zero error on labeled points and well-separated unlabeled points.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.