Machine Learning with Julia

Jeremiah D. Deng is an associate professor in School of Computing at University of Otago, New Zealand. His research interests include pattern recognition, machine learning, and stochastic optimization. He has published at top-tier venues such as PR, NN, TC, TEC, TKDE, TBE, and IJCAI, and serves on the editorial boards of Pattern Analysis and Applications (Springer) and ICT Express (Elsevier) and on the program committees of various AI conferences. Dr. Deng completed his PhD in computer science at University of Hong Kong and South China University of Technology, and has held visiting and adjunct positions at University of Adelaide and South China University of Technology. He is a Senior Member of both IEEE and ACM.

• Introduction.- Metrics and Divergences.- Clustering.- Online Clustering.- Dimension Reduction.- Bayesian classification.- Support Vector Machines = Linear Machines + Kernels.- Tree and Forest: Divide-and-Conquer.- Regression and Model Selection.- Ensemble Methods.- Neural networks.- Convolutional neural networks.- Autoencoders.- Generative adversarial networks.- Transfer Learning.- Federated Learning.