Chris Pollett > CS256
( Print View )

Student Corner:
  [
Submit Sec1]
  [Grades Sec1]

  [Lecture Notes]

  [Discussion Board]

Course Info:
  [Texts & Links]
  [Topics/Outcomes]
  [Outcomes Matrix]
  [Grading]
  [HW/Quiz Info]
  [Exam Info]
  [Regrades]
  [Honesty]
  [Additional Policies]
  [Announcements]

HWs and Quizzes:
  [Hw1]  [Hw2]  [Hw3]
  [Hw4]  [Hw5]  [Quizzes]

Practice Exams:
  [Mid 1]  [Mid 2]  [Final]

                           












Learning Outcomes versus Collected Course Materials (Sec 1)
CLO1CLO2CLO3CLO4CLO5CLO6CLO7N/A
HW1XXX
HW2XXX
HW3XXX
HW4XXXX
HW5XXXX
M1P1X
M1P2X
M1P3X
M1P4X
M1P5X
M2P1X
M2P2X
M2P3X
M2P4X
M2P5X
F1
F2
F3
F4
F5
F6
F7
F8
F9
F10

Legend

MiPj -- 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.