Course Syllabus

At the end of this course, students will be able to:

• convert a structured data set (like text) into an abstract data representation such as a vector, a set, or a matrix, with modeling considerations, for use in downstream data analysis
• implement and analyze touchstone data mining algorithms for clustering, dimensionality reduction, regularized regression, graph analysis, and locality sensitive hashing.
• understand, discuss, and evaluate advanced data mining algorithms for clustering, dimensionality reduction, regularized regression, graph analysis, locality sensitive hashing, and managing noisy data.
• work with a team to design and execute a multi-faceted data mining project on data that is not already structured for the analysis task, and to compare and evaluate the design choices.
• present progress and final results using written, oral, and visual media on a data analysis project to peers in small groups, to peers in a large interactive environment, and to get approval from a superior.

The book for this course will mostly be a nearly-complete book on the Mathematical Foundation for Data Analysis (M4D), version v0.6. However, the lectures will follow more closely Jeff Phillips' related Data Mining course notes, and in several cases, these have not made it into the above book (yet?).

We will also often link to two other online resources that cover similar material, either with a more applied or theoretical focus:

• MMDS(v1.3): Mining Massive Data Sets by Anand Rajaraman, Jure Leskovec, and Jeff Ullman. The digital version of the book is free, but you may wish to purchase a hard copy.
• FoDS: Foundations of Data Science by Avrim Blum, John Hopcroft and Ravindran Kannan. This provide some proofs and formalisms not explicitly covered in lecture.

We will use Canvas for Announcements, sharing Assignments (not turning them in!), and communicating grades.

You will use Gradescope for submitting assignments. 

We will use Piazza for all communication: from questions on homework, projects, material covered in class, etc. Here is a link: https://piazza.com/utah/spring2023/cs6140001spring2023. 

You can post anonymously to other students, but your name will always be visible to the instructors. 

Our goal is to avoid answering the same questions over and over to individual students and respond to everyone in a timely manner, without running out of time for the rest of our daily tasks. Please post public posts whenever possible. 

You can reach out to us privately on Piazza by posting a post of the type note and selecting "Post to" ->  "Instructors" and including TAs. This is a preferred way to reach us privately – please do not send emails or a message on Canvas because then your messages get mixed up with everything else [research, service, etc.] and it's hard to prioritize you. Please reach out to us directly only if necessary. 

I will endorse TAs messages and this effectively means: I have nothing to add to this. 

Besides posting questions in Piazza, feel free to answer questions but do not post potential homework answers. Such posts will be immediately removed, and not answered.

All other activities, including the schedule, links to class notes, and links to videos, will be through the public-facing website: https://utah-data-mining-spring23.github.io.

Please address the instructor as Professor or Professor Marasović. You can also reach your instructor/TAs in the following ways:

1. 1. Come to the office hours. Please send us a private post on Piazza if you need to meet outside of office hours.
      
      Who: Prof. Marasović + all TAs
      When/Where: Mondays 4:30p (MEB 3115) / Wednesdays 4:30p (MEB 3515)
      
      Who: Zhichao Xu (TA)
      When/Where: Tuesdays 4-5p (MEB 3105) & Wednesdays 1-2p (Zoom)
      Zoom meeting room: https://utah.zoom.us/j/94851553181 (Meeting ID: 948 5155 3181; Passcode: 745498)
      
      Who: Vinutha Raja (TA)
      When/Where: Tuesdays 12-1p (Zoom) & Thursdays  2:30-3:30p (Zoom)
      Zoom meeting room: https://utah.zoom.us/j/7484553531 (Meeting ID: 748 455 3531; Passcode: 504330)
      
      Who: Nathan Stringham (TA) 
      When/Where: Mondays 12-1p (MEB 3115) & Wednesdays 11-12p (MEB 3515) 
      
      
   2. Ask in class or after each lecture.

Take advantage of the instructor and TA office hours. We will work hard to be accessible to students. Don’t be shy if you don’t understand something: come to office hours, send emails, or speak up in class!

Your performance in this course will be evaluated by:

• homework assignments (40%)
• two exams (20%)
• projects (40%)

Homework assignments: We will plan to have 8 short homework assignments, roughly covering each main topic in the class. The homeworks will usually consist of an analytical problems set, and sometimes a programming exercise. A preferred programming language for the class is Python.

Exams: There will be two tests, each covering roughly half the material in class. They will be open notes; you can bring 1 sheet of paper (front and back). No computers or calculators will be allowed. No exam may be taken at a different time for any reason other than a medical emergency or conflict with another exam, and documentation may be required.

Projects: Each person in the class will be responsible for a group project. The project will be very open-ended; basically, it will consist of finding an interesting data set, exploring it with one or more techniques from class, and presenting what you found. I will try to provide suggestions for data sources and topics, but ultimately the groups will need to decide on their own topic. There will be several intermediate deadlines so projects are not rushed at the end of the semester. Details of the project requirements can be found at https://utah-data-mining-spring23.github.io/assignments/project/. 

Exceptions for undergraduate students: 

1. Undergraduate students are allowed to do written reports for all paper discussion sessions, but they must choose in the first two weeks whether they will do that or also participate in regular role-play presentations. 
   
   
2. The lowest-scoring homework assignment will be dropped. That is, the homework percentage will be calculated with (the sum of scores of the remaining three homeworkers)/300.
   
   
3. Undergraduate students are encouraged to conduct a user study on Nov 22, but they are not required to and the outcomes of the user study will be considered for their project grade only as bonus points. That said, they still must participate in the preparation of the user study on Nov 20 and use dummy instances instead of actual ones if they don't have them.

Due dates for all assignments can be found at the bottom of this syllabus.

Standard due date times for homeworks are 2:45 PM. Different aspects of projects are due at different times, so please carefully review when each component is due (either on Canvas and/or at https://utah-data-mining-spring23.github.io/assignments/project/).

All assignments, unless otherwise announced, must be submitted to the designated area of Canvas. Do not submit assignments via email.

I recommend using LaTex for writing up homeworks. It is something that everyone should know for research and writing scientific documents. This overleaf project contains a sample .tex file, as well as what its .pdf compiled outcome looks like. It also has a figure .pdf to show how to include figures. 

The class operates under the School of Computing’s policies and guidelines (see below). Among other things, we will adhere to the academic misconduct policy. More information about the policy can be found on the linked page, but we highlight this: 

Violations of this policy are recorded as “strikes” by the SoC. A failing grade sanction in an SoC course counts as one “strike” in a student’s academic record. Two lesser sanctions in SoC courses count as one “strike” in a student’s academic record. Any student with two strikes due to academic misconduct will be subsequently barred from registering for any additional SoC courses, immediately dropped from their respective degree program, and will not be admitted to any future SoC program.

You are encouraged to discuss class materials with your peers. If you want you can form study groups because discussions help understanding. You are also welcome to discuss assignments.

However, you must write your own solutions, proofs, and code and submit your own solution. Do not copy or ask for assignments from other students or the internet. Do not let someone else copy your submissions either.

You should cite all sources that you refer to. This includes personal communication, books, papers, websites, etc. Doing so reflects academic integrity.

To get full credit for an assignment, it must be turned in through Canvas by the due time.  

Late homework. Standard due date times for homework assignments are 2:45 PM. You are allowed to be late with at most two HOMEWORK assignments no more than 48H, without penalty. You don't need to ask for permission. After two homework assignments have been submitted late, once the deadline is missed, those turned in late will lose 10%. Every subsequent 24 hours until it is turned another 10% is deducted. Assignments will not be accepted more than 48 hours late and will be given a 0.

Late project components. Different aspects of projects are due at different times, so please carefully review when each component is due (see https://utah-data-mining-spring23.github.io/assignments/project/). For PROJECT assignments, the same penalty as for homework is applied for every component of the project that is submitted late (i.e., you can NOT be late with two project components without penalty). Each team member gets deducted scores for late project components.  

Assignments will be posted far enough ahead of time that I will not be able to make additional exceptions if a student falls ill. The exception will be prolonged illness accompanied by a doctor's note. 

If you believe there is an error in grading (homeworks or exams), you may request a regrading within one week of receiving your grade. Requests must be made by email to the instructor, explaining clearly why you think your solution is correct.

You can earn extra points on homework assignments and on the poster presentation. No additional chances for earning extra credit will be granted after the final grades are shared. 

The final grades will be posted by May 15. 

I plan to map numerical grades to letter grades at the standard scale:

Disclaimer

Accommodations will be considered on an individual basis and may require documentation.

Please contact your instructor and/or teaching assistant as soon as possible (preferably shortly before the semester begins) to request accommodations of any kind.

Extreme personal circumstances

Please contact your instructor as soon as possible if an extreme personal circumstance
(hospitalization, death of a close relative, natural disaster, etc.) is interfering with your ability to
complete your work.

Religious Practice

To request an accommodation for religious practices, contact your instructor at the beginning of the semester.

Active Duty Military

If you are a student on active duty with the military and experience issues that prevent you from participating in the course because of deployment or service responsibilities, contact your instructor as soon as possible to discuss appropriate accommodations.

Disability Access

All written information in this course can be made available in an alternative format with prior notification to the Center for Disability Services (CDS). CDS will work with you and the instructor to make arrangements for accommodations. Prior notice is appreciated. To read the full accommodations policy for the University of Utah, please see Section Q of the Instruction & Evaluation regulations.

If you will need accommodations in this class, contact:

Center for Disability Services
  801-581-5020
  disability.utah.edu
  162 Union Building
    200 S. Central Campus Dr.
     Salt Lake City, UT 84112

This syllabus is not a contract. It is meant to serve as an outline and guide for your course. Please note that your instructor may modify it to accommodate the needs of your class.

You will be notified of any changes to the Syllabus.

Though the online format allows students greater flexibility to complete their work, this course does have a structure and timeline! As such, the following is expected of all students in this class:

• Students must be self-motivated, organized, and willing to stay on top of their schedules. Students should take control of their learning while in this course.
• Students are expected to follow the Core Rules of Netiquette at all times while participating in the class and communicating with others.
• Students will log in to the course a minimum of 3 times per week.
• Students are not expected to interact with their classmates in person. Students may be expected to work with classmates via online communication options like Canvas Discussions, video conferencing, or other communication technologies of choice (Groupme, FaceTime, Google Hangouts, etc).
• Students will regularly check for course updates and will update their Canvas notification settings to ensure they receive timely notifications from the course.
• Students will contact their instructor or teaching assistant in a timely manner if they have any questions, are struggling with course materials, or need further assistance from their instructor.
  • If you do not hear back within 3 days after sending a message, please contact your instructor/TA again.
• Students will follow all official University of Utah policies regarding interpersonal conduct, academic dishonesty, and other rights and responsibilities of students outlined in the University of Utah Student Handbook and Code of Student Rights and Responsibilities.
  • If you have any questions about this, please contact the Dean of Students.

Your course instructor is an expert in the topics you will learn about this semester. Your instructor is your mentor and facilitator of the classroom experience, aided by teaching assistants. Instructors are committed to:

• The instructor will design the course to include lectures, learning materials, and assignments that are accessible and provide students with opportunities to learn and practice course content.
• The instructor and teaching assistants will ensure that the course remains a safe space where students can engage with difficult content thoughtfully and respectfully.
• The instructor and teaching assistants will interact with the class regularly via announcements, virtual office hours (one-on-one video conferencing), emails/the Canvas Inbox, feedback on assignments, and comments on Discussions, among other methods.
• The instructor and teaching assistants will respond to students in a timely manner: within 48 hours, not including weekends and holidays.
• The instructor and teaching assistants will be available for an individual consultation via virtual office hours (one-on-one video conferencing), email, or phone and will not require students to meet in person.
• The instructor and teaching assistants will provide relevant feedback in a timely manner.
• The instructor and teaching assistants will follow all official University of Utah policies regarding interpersonal conduct, accommodations, and other important duties.