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syllabus [2020/01/14 15:40]
127.0.0.1 external edit
syllabus [2020/03/31 15:29] (current)
anderson [Grading]
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   * use the latest features in python, including jupyter notebooks.   * use the latest features in python, including jupyter notebooks.
  
-For implementations we will be using [[https://​www.python.org/​|Python]]. Previous experience with Python and its numpy package is helpful. ​ To prepare for this course, please download and install Python on your computer, and work through on-line tutorials to help prepare for this course. ​ The [[https://​anaconda.org/|Anaconda distribution]] is recommended,​ which is a free download for all platforms. ​+For implementations we will be using [[https://​www.python.org/​|Python]]. Previous experience with Python and its numpy package is helpful. ​ To prepare for this course, please download and install Python on your computer, and work through on-line tutorials to help prepare for this course. ​ The [[https://​anaconda.com/distribution|Anaconda distribution]] is recommended,​ which is a free download for all platforms. ​
  
 Class meetings will be a combination of lectures by the instructor and discussions of students'​ questions. All questions are welcome, no matter how simple you think they are; it is always true that someone else has a similar question. It is critical that everyone in class respect each other when questions are asked and answers are suggested and at all times. Please contact the instructor when you have felt disrespect from other students, the instructor, or the graduate teaching assistants. This may be anonymous if you prefer. Class meetings will be a combination of lectures by the instructor and discussions of students'​ questions. All questions are welcome, no matter how simple you think they are; it is always true that someone else has a similar question. It is critical that everyone in class respect each other when questions are asked and answers are suggested and at all times. Please contact the instructor when you have felt disrespect from other students, the instructor, or the graduate teaching assistants. This may be anonymous if you prefer.
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 ===== Time and Place ===== ===== Time and Place =====
  
-Class meets every Tuesday and Thursday, 2:00 - 3:15 PM in [[https://​www.fm.colostate.edu/​sites/​default/​files/​maps/​0190-01-Classroom.pdf|Stadium ​Room 1205]].  On-campus and distance-learning students will be able to watch video recordings of lectures through our Canvas webpage.+**On-line Procedure** 
 + 
 +Starting with class on March 26th, our lectures and office hours went on-line. ​ They will be on the same days and times: lectures 2:00 to 3:15 PM Tuesday and Thursday, and office hours will be 10:00 AM to 12:00 Tuesdays. ​ We are using Microsoft Teams to meet.  Please let me know (chuck.anderson@colostate.edu) if you are unable to join the live meetings. ​ Recorded lectures will continue to be available on Canvas at the Echo360 page. 
 + 
 +**Old time and place** 
 + 
 +Class meets every Tuesday and Thursday, 2:00 - 3:15 PM in [[https://​map.concept3d.com/?​id=748#​!m/​122441?​ce/​9550?​ct/​25059,​20556,​20377,​13646,​13645,​13644,​12106,​9554|Wagar Building]] ​[[https://​www.fm.colostate.edu/​sites/​default/​files/​maps/​0084-01.pdf|Room ​133]].  On-campus and distance-learning students will be able to watch video recordings of lectures through our Canvas webpage.
  
 ===== Prerequisites ===== ===== Prerequisites =====
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 There are no required text books for this course. ​ Readings may be assigned from the following on-line books. There are no required text books for this course. ​ Readings may be assigned from the following on-line books.
 +
  
 [[http://​www.labri.fr/​perso/​nrougier/​from-python-to-numpy/​|From Python to Numpy]] by Nicolas P. Rougier ​ [[http://​www.labri.fr/​perso/​nrougier/​from-python-to-numpy/​|From Python to Numpy]] by Nicolas P. Rougier ​
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 [[http://​incompleteideas.net/​book/​the-book.html|Reinforcement Learning: An Introduction]],​ by Richard Sutton and Andrew Barto, 2nd edition [[http://​incompleteideas.net/​book/​the-book.html|Reinforcement Learning: An Introduction]],​ by Richard Sutton and Andrew Barto, 2nd edition
 +
 +[[https://​mml-book.com|Mathematics for Machine Learning]] by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong
 +
 ===== Instructors ===== ===== Instructors =====
  
 ^    ^  Office ​ ^  Hours  ^  Contact ​ | ^    ^  Office ​ ^  Hours  ^  Contact ​ |
-^  [[http://​www.cs.colostate.edu/​~anderson|Chuck Anderson]] ​ |  Computer Science Building (CSB) Room 444  |    ​Wednesdays\\ 12:00 - 3:00 PM    ​| ​ chuck.anderson@colostate.edu\\ ​ 970-491-7491 ​ | +^  [[http://​www.cs.colostate.edu/​~anderson|Chuck Anderson]] ​ |  Computer Science Building (CSB) Room 444  |    ​CSB Room 444\\ Tuesday\\ 10:00 AM 12:00\\ or by appointment ​   ​| ​ chuck.anderson@colostate.edu\\ ​ 970-491-7491 ​ | 
-^  GTA: Ameni Trabelsi ​ |  CSB 235  ​| ​   |  ameni.trabelsi@colostate.edu ​  | +^  GTA: Ameni Trabelsi ​ |  CSB Room 415  ​| ​ CSB Room 120\\ Monday & Wednesday 10:00 AM - 12:​00  ​|  ameni.trabelsi@colostate.edu ​  | 
-^  GTA: Wen Qin   ​| ​ CSB   ​| ​    |      ​|+^  GTA: Wen Qin   |    ​|  CSB Room 120\\ Wednesday & Thursday\\ 8:00 AM - 10:00   |   wen.qin@colostate.edu ​   ​|
  
  
 ===== Grading ===== ===== Grading =====
 +
 +** New grading scheme **
 +
 +The final project is now optional. ​ If you choose to submit a project, the grade you receive on the project will replace your two lowest assignment grades. ​ We will have at least seven total assignments. ​ So, you may choose to do a final project and not turn in the last two assignments and have the final project grade replace those two assignment grades. ​ If you choose to not submit a project, your grade will be based only on your assignment grades.
 +
 +Honors students have this option, also.  If an Honors student chooses to submit a final project, they must do all of the extra-credit parts of all but two assignments that they choose. ​ Otherwise, an Honors student must complete all extra-credit parts.
 +
 +You will be assigned a letter grade for the semester. ​ You can then choose to convert it to S/U (Sastisfactory/​Unsatisfactory) grading as described in [[https://​www.acns.colostate.edu/​media/​sites/​100/​2020/​03/​Spring-2020-SU-gradingv2.pdf|this link]].
 +
 +
 +** Old grading scheme **
  
 Your grade for this course will be based only on the assignments,​ most of which will require the submission of a jupyter notebook that includes text descriptions of your methods, results and conclusions and the python code for defining machine learning algorithms, loading data and applying your algorithms to the data.  Each notebook will be graded for correct implementation and results, thorough discussion of your code and observations of results, and good organization,​ grammar and spelling. ​ No quizzes or exams will be given. Your grade for this course will be based only on the assignments,​ most of which will require the submission of a jupyter notebook that includes text descriptions of your methods, results and conclusions and the python code for defining machine learning algorithms, loading data and applying your algorithms to the data.  Each notebook will be graded for correct implementation and results, thorough discussion of your code and observations of results, and good organization,​ grammar and spelling. ​ No quizzes or exams will be given.
syllabus.1579041645.txt.gz · Last modified: 2020/01/14 15:40 by 127.0.0.1