| 01 | -T-R--- | 2:30P-3:50P | Remote / LA | Nussinov | Default - none | 15 | 5 | 0 |
Desc: | Algorithms of Machine Learning: Topics include: Review of several basic notions from statistical mechanics, spin glass physics, information theory, complexity theory, and networks. Principal Component Analysis, Neural networks, Markov Chains, Loss functions and back-propagation, Steepest descent type methods including AdaGrad, Nesterov Accelerated Gradient Descent, Convolution networks, Bayesian inference and an application in machine learning. Clustering and unsupervised machine learning and some applications in image segmentation and several physics problems. Swarm Optimization and Ensemble Methods, Reinforcement learning, Generative adversarial networks. The final will that of be projects of students (which they will present to the class). Pre-requisites: The students should be able to program in at least one computer language so as to independently implement the algorithms taught in class and do their final projects. 3 Units. |
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