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syllabus [2020/08/23 15:29]
anderson [Time and Place]
syllabus [2021/05/17 14:53] (current)
anderson [Grading]
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 ===== Description ===== ===== Description =====
  
-This course covers fundamental and advanced concepts and methods involving deep neural networks for solving problems in data classification, prediction, visualization, and reinforcement learning. Students will learn how to+This course reviews fundamental methods and covers advanced concepts and methods involving deep neural networks for solving problems in data classification, prediction, visualization, and reinforcement learning.  
 + 
 +Students will //review// how to
  
   * read data files of various formats and visualize characteristics of the data,   * read data files of various formats and visualize characteristics of the data,
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   * develop and apply classification algorithms to classify multivariate data,   * develop and apply classification algorithms to classify multivariate data,
   * develop and apply regression algorithms for finding relationships between data variables,   * develop and apply regression algorithms for finding relationships between data variables,
-  * develop and apply reinforcement learning algorithms for learning to control complex systems, 
-  * write scientific reports on computational machine learning methods, results and conclusions, 
   * use the latest features in python, including jupyter notebooks, and   * use the latest features in python, including jupyter notebooks, and
   * how to repeat experiments described in on-line tutorials, documentation, and publications in deep learning.   * how to repeat experiments described in on-line tutorials, documentation, and publications in deep learning.
 +
 +Students will //learn// how to
 +
 +  * formulate and derive new algorithms for deep neural networks,
 +  * develop and apply reinforcement learning algorithms for learning to control complex systems,
 +  * interpret what a deep neural network has learned,
 +  * investigate the true advantages and limitations of recently developed, popular, complex deep network methods compared to simpler, older approaches,
 +  * write scientific reports on computational machine learning methods, results and conclusions, and
 +  * how to design, conduct, and report on novel machine learning experiments.
  
 For implementations we will be using For implementations we will be using
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 ===== Time and Place ===== ===== Time and Place =====
  
-Class meets every Tuesday and Thursday, 12:30 PM - 1:45 PM, **on-line as +Class meets every Tuesday and Thursday, 12:30 PM - 1:45 PM, in Eddy Room 10.   
-a Microsoft Teams meeting** that you can find [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598126507312?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|at this link]].  You may download Microsoft Teams apps for Windows, Mac, and Linux from [[https://docs.microsoft.com/en-us/microsoftteams/get-clients|this link at Microsoft]].+ 
 +The on-line section will be available through Canvas.
  
 ===== Prerequisites ===== ===== Prerequisites =====
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 ^    ^  Office  ^  Hours  ^  Contact  | ^    ^  Office  ^  Hours  ^  Contact  |
-^  [[http://www.cs.colostate.edu/~anderson|Chuck Anderson]]  |  Computer Science Building (CSB) Room 444   Wednesdays\\ 10 AM - 12\\ Microsoft Teams     |  chuck.anderson@colostate.edu\\  970-491-7491 +^  [[http://www.cs.colostate.edu/~anderson|Chuck Anderson]]  |  to be determined     
-^  GTA: ??  |    |      |+ chuck.anderson@colostate.edu\\  970-491-7491 
 +^  GTA: \\ to be determined     to be determined |
  
  
 ===== Grading ===== ===== Grading =====
  
-Your grade for this course will be based on the assignments, most+Your grade for this course will be based on six to eight assignments, each
 of which will require the submission of a jupyter notebook that of which will require the submission of a jupyter notebook that
 includes text descriptions of your methods, results and conclusions includes text descriptions of your methods, results and conclusions
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 and spelling.  No quizzes or exams will be given. and spelling.  No quizzes or exams will be given.
  
-Six to seven regular assignments are planned during the semester. In +A final semester project of your own design might be required This will be determined prior to the start of the fall semester. If semester project is required, the grade on the project will have as much weight as two assignments.
-total these will count for 70% of your semester gradeThe final +
-assignment is a project designed by you and is worth 20% of your +
-semester grade. These percents are summarized in the following list. +
- +
-  * 80% regular assignmentsfrom 10% to 16% each +
-  * 2% for the project proposal +
-  * 18% for the project written report+
  
 Semester letter grades will include plus and minus grades.  Assume Semester letter grades will include plus and minus grades.  Assume
syllabus.1598218148.txt.gz · Last modified: 2020/08/23 15:29 by anderson