Chris Pollett >
Old Classes >
CS256 |
LegendMiPj -- Midterm i problem j Fi -- Final problem i Each class had distinct versions of all exams. Within a class there were also two versions of a given test; however, these two versions were just problem permutations of each other. The results above are all for the second of these two permutations. CLO1 -- Be able to code without a library a single perceptron training algorithm. CLO2 -- Be able to predict the effect of different activation functions on the ability of a network to learn. CLO3 -- Be able to explain how different neural network training algorithms work. CLO4 -- Be able to select neural network layers type to build a network suitable for various learning tasks such as object classification, object detection, language processing, planning, policy selection, etc. CLO5 -- Be able to select an appropriate regularization technique for a given learning task. CLO6 -- Be able to code and train with a library such as Caffe, Theano, Tensorflow a multi-layer neural network. CLO7 -- Be able to measure the performance of a model, determine if more data in needed, as well as how to tune the model. N/A -- Important material covered in the course but not directly related to a specific learning outcome. |