Chris Pollett> CS256
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Course Info:
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[Description]
[Course Outcomes]
[Outcomes Matrix]
[Course Schedule]
[Grading]
[Requirements/HW/Quizzes]
[Class Protocols]
[Exam Info]
[Regrades]
[University Policies]
[Announcements]

HW Assignments:
[Hw1] [Hw2] [Hw3]
[Hw4] [Hw5] [Quizzes]

Practice Exams:
[Midterm] [Final]

Learning Outcomes versus Collected Course Materials
CLO1CLO2CLO3CLO4CLO5CLO6CLO7N/A
HW1XXX
HW2XXX
MTP1XXX
MTP2XX
MTP3X
MTP4X
MTP5XX
HW3XXXX
HW4XXXX
HW5XXXX

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 Tensorflow or Pytorch 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.