Division of Teaching Excellence and Innovation UC Irvine
Friday, October 5, 2018 - 4:00pm
In this session, we will cover the use of active learning techniques in the classroom to engage students in the learning process. We will begin with a short discussion on considerations for active learning, followed by how to create buy-in for students. Afterwards, we will go over different techniques depending on content goals and group sizes. We will finish by designing a lesson plan that integrates 1-2 active learning techniques that you can use in your own discussions. Participants interested in learning more are encouraged to visit the Division of Teaching Excellence and Innovation (DTEI) website at www.dtei.uci.edu.
In this talk, we shall give a mathematical setting of the Random Backpropogation (RBP) method in unsupervised machine learning. When there is no hidden layer in the neural network, the method degenerates to the usual least square method. When there are multiple hidden layers, we can formulate the learning procedure as a system of nonlinear ODEs. We proved the short time, long time existences as well as the convergence of the system of nonlinear ODEs when there is only one hidden layer. This is joint work with Pierre Baldi in Neural Networks 33 (2012) 136-147, and with Pierre Baldi, Peter Sadowski in Neural Networks 95 (2017) 110-133 and in Artificial Intelligence 260 (2018), 1-35.