CS291A Introduction to Pattern Recognition, Artificial Neural Networks, and Machine Learning
This course will cover topics on pattern recognition (PR), artificial neural networks (ANN), and machine learning (ML). Pattern recognition is a classical research area that deals with recognizing patterns (objects) based on their features (traits or appearance). It has seen wide applications in speech recognition, image analysis, target detection, optical character recognition, fingerprint identification, dating services, insurance fraud detection, DNA sequence alignment, protein structure matching, data mining, network intrusion detection, engine trouble shooting, among many others. Artificial neural networks provide a general computing framework which is purported to be highly parallel, distributed, and fault tolerant. Machine learning deals with algorithms and formulations that enable a machine to learn and improve its performance from experience, that is, to modify its behaviors and execution on the basis of acquired information and data analysis. These areas share a lot of commonality in addressing similar problems in classification and clustering. The particular incarnation next quarter will emphasize the emerging area of deep learning using convolutional and recurrent neural networks, in particular, for image andcomputer vision applications.