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syllabus [2020/08/24 11:03]
anderson [Instructors]
syllabus [2021/10/06 13:00] (current)
anderson [Instructors]
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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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 penalty in this course and a report to the Office of Student penalty in this course and a report to the Office of Student
 Resolution Center. Resolution Center.
 +
 +Important information for students: All students are expected and
 +required to report any COVID-19 symptoms to the university
 +immediately, as well as exposures or positive tests from a non-CSU
 +testing location.
 +
 +If you suspect you have symptoms, or if you know you have been exposed
 +to a positive person or have tested positive for COVID, you are
 +required to fill out the [[https://covid.colostate.edu/reporter/|COVID
 +Reporter]]. If you know or believe you have been exposed, including
 +living with someone known to be COVID positive, or are symptomatic, it
 +is important for the health of yourself and others that you complete
 +the online COVID Reporter. Do not ask your instructor to report for
 +you. If you do not have internet access to fill out the online
 +COVID-19 Reporter, please call (970) 491-4600. You may also report
 +concerns in your academic or living spaces regarding COVID exposures
 +through the COVID Reporter. You will not be penalized in any way for
 +reporting. When you complete the COVID Reporter for any reason, the
 +CSU Public Health office is notified. Once notified, that office will
 +contact you and, depending upon each situation, will conduct contact
 +tracing, initiate any necessary public health requirements and notify
 +you if you need to take any steps.
 +
 +For the latest information about the University’s COVID resources and
 +information, please visit this [[https://covid.colostate.edu/|CSU COVID-19 site]].
 +
  
  
 ===== 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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 ===== Instructors ===== ===== Instructors =====
  
-^    ^  Office  ^  Hours   Contact  | +^    ^  Office Hours   
- [[http://www.cs.colostate.edu/~anderson|Chuck Anderson]]   Computer Science Building\\ Room 444  |  10 11am\\ [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598288422204?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|MS Teams Link]]    |  chuck.anderson@colostate.edu\\  970-491-7491  +^ Chuck Anderson | Tuesdays: 9:00 - 10:00\\ Thursdays 11:3012:15\\ Please fill out and submit [[https://forms.gle/P6cWq9zDR8m6BSEr9|this form]] at the start\\ of office hours to enable a personal zoom meeting.\\ You may also send me e-mail (chuck.anderson@colostate.edu) to set up appointment or with questions.   |  
-^  GTA: \\ [[https://www.linkedin.com/in/markovikj/|Dejan Markovikj]]     |    Dejan.Markovikj@colostate.edu  |+^  GTA: Dejan Markovikj  | Mondays: 11:00 - 1:00\\  Wednesdays: 3:00 - 5:00\\ Please fill out and submit [[https://forms.gle/P6cWq9zDR8m6BSEr9|this form]] at the start\\ of office hours to enable a personal zoom meeting.  |
  
  
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 graded for correct implementation and results, thorough discussion of graded for correct implementation and results, thorough discussion of
 your code and observations of results, and good organization, grammar your code and observations of results, and good organization, grammar
-and spelling.  No quizzes or exams will be given.+and spelling. 
 +No quizzes or exams will be given
 + 
 +A final semester project of your own design might be required.  This will be determined prior to the start of the fall semester. If a semester project is required, the grade on the project will have as much weight as two assignments.
  
 Semester letter grades will include plus and minus grades.  Assume Semester letter grades will include plus and minus grades.  Assume
syllabus.1598288589.txt.gz · Last modified: 2020/08/24 11:03 by anderson