syllabus
Differences
This shows you the differences between two versions of the page.
Previous revision | |||
syllabus [2022/09/06 09:35] – [Instructors] anderson | — | ||
---|---|---|---|
Line 1: | Line 1: | ||
- | ====== Overview ====== | ||
- | |||
- | If you are having trouble registering for the on-campus section, you are welcome to [[https:// | ||
- | |||
- | This Overview is out-of-date. | ||
- | |||
- | |||
- | |||
- | ===== Description ===== | ||
- | |||
- | This course reviews fundamental methods and covers advanced concepts and methods involving deep neural networks for solving problems in data classification, | ||
- | |||
- | Students will //review// how to | ||
- | |||
- | * read data files of various formats and visualize characteristics of the data, | ||
- | * perform statistical analyses on multivariate data, | ||
- | * develop and apply classification algorithms to classify multivariate data, | ||
- | * develop and apply regression algorithms for finding relationships between data variables, | ||
- | * use the latest features in python, including jupyter notebooks, and | ||
- | * how to repeat experiments described in on-line tutorials, documentation, | ||
- | |||
- | 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, | ||
- | * how to design, conduct, and report on novel machine learning experiments. | ||
- | |||
- | For implementations we will be using | ||
- | [[https:// | ||
- | and its numpy package is helpful. | ||
- | download and install Python on your computer, and work through on-line | ||
- | tutorials to help prepare for this course. | ||
- | [[https:// | ||
- | recommended, | ||
- | |||
- | CS440 is the prerequesite for this course. | ||
- | more relevant background for the material in CS545. | ||
- | will be revisited in CS545. | ||
- | CS445 is the scale of the assignments, | ||
- | Pytorch and Tensorflow, and discussions of recent papers in the | ||
- | research literature on deep learning. | ||
- | |||
- | Class meetings will be a combination of lectures by the instructor, | ||
- | discussions of students' | ||
- | class. 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. | ||
- | |||
- | A lot of material will be covered in this course. Students are | ||
- | expected to speak up in class with questions and observations they | ||
- | have about the material. Do not expect to be able to complete all | ||
- | assignments working on your own and without asking any questions. If | ||
- | you find yourself wondering what the next step is in finishing an | ||
- | assignment, please feel free to e-mail the instructor. You may also | ||
- | discuss assignments with other students directly, or via our Piazza | ||
- | Discussion board, but your code and report must be written by you. Do | ||
- | not post code on the Piazza Discussion board as part of your question | ||
- | nor as part of your answer. | ||
- | |||
- | You are expected to be familiar with the | ||
- | [[http:// | ||
- | policy]] on cheating and with the CS Department Code of Conduct found | ||
- | at the department' | ||
- | [[http:// | ||
- | Resources]] web page. This course will adhere to the CSU Academic | ||
- | Integrity Policy as found on the Student Responsibilities page of the | ||
- | [[http:// | ||
- | General Catalog]] and in the | ||
- | [[https:// | ||
- | Conduct Code]]. | ||
- | penalty in this course and a report to the Office of Student | ||
- | Resolution Center. | ||
- | |||
- | |||
- | ===== Time and Place ===== | ||
- | |||
- | Class meets every Tuesday and Thursday, 11:00 AM - 12:15 PM, in Computer Science Building 130. | ||
- | |||
- | The on-line section will be available through Canvas. | ||
- | |||
- | ===== Prerequisites ===== | ||
- | |||
- | CS440 with a grade of C or better, and some experience with python. | ||
- | |||
- | ===== Textbook ===== | ||
- | |||
- | There are no required text books for this course. | ||
- | |||
- | [[http:// | ||
- | |||
- | [[http:// | ||
- | |||
- | [[http:// | ||
- | |||
- | ===== Instructors ===== | ||
- | |||
- | These hours and GTA are not current and will be updated! | ||
- | |||
- | ^ ^ ^ Office Hours ^ | ||
- | ^ Chuck Anderson | chuck.anderson@colostate.edu | ||
- | ^ GTA: Saira Jabeen | ||
- | |||
- | |||
- | ===== Grading ===== | ||
- | |||
- | Your grade for this course will be based on about six assignments, | ||
- | 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, | ||
- | and spelling. | ||
- | We might have quizzes, but we will not have exams. | ||
- | |||
- | A final semester project of your own design will be required. | ||
- | |||
- | /*** | ||
- | **Since we dropped the lowest assignment grade, too much weight will be applied to the final project. | ||
- | ** In other words, your four best assignments determine 72% of your grade, your project report determines 24% of your grade, and your proposal determines 4% of your grade. | ||
- | ***/ | ||
- | |||
- | |||
- | Semester letter grades will include plus and minus grades. | ||
- | standard ranges of numerical grades will be used. Ranges might be | ||
- | shifted a little lower, depending on the grade distribution at the end | ||
- | of the semester, but they will not be raised. | ||
- | |||
- | Some assignments will include extra credit points. | ||
- | accumulated during the semester. | ||
- | grade is just below a letter grade cutoff, the extra credit points | ||
- | might push you up to the higher grade. | ||
- | roughly worth 1/2 a percent. | ||
- | the cutoff or A- is 90%, four extra credit points will bump your grade | ||
- | up to an A-. | ||
- | |||
- | **Late reports will not be accepted**, unless you make arrangements with the instructor at least two days before the due date. | ||
- | |||
- | ===== COVID-19 ===== | ||
- | |||
- | All students are expected and required to report any COVID-19 symptoms to the university | ||
- | immediately, | ||
- | |||
- | • If you suspect you have symptoms, or if you know you have been exposed to a positive person or | ||
- | have tested positive for COVID (even with a home test), you are required to fill out the [[https:// | ||
- | Reporter]]. | ||
- | |||
- | • If you know or believe you have been exposed, including living with someone known to be COVID | ||
- | positive, or are symptomatic, | ||
- | 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. | ||
- | Students who report symptoms or a positive antigen test through the COVID Reporter may be | ||
- | directed to get a PCR test through the CSU Health Network’s medical services for students. | ||
- | |||
- | For the latest information about the University’s COVID resources and information, | ||
- | COVID-19 site]]. | ||
syllabus.txt · Last modified: 2023/09/06 15:22 by anderson