Machine Learning

Yes. This page can be long and boring. I know because I made it. But I encourage you to read it at least once early in the semester.

1. Meetings & staff
2. Course objectives
3. Prerequisites
4. Grading
5. Course policies
6. Student support
7. Communication with teaching staff and getting help
8. Textbooks and resources

Meetings & Staff

Lectures: Tue & Thu, 12:25 PM – 1:45 PM, WEB L101. See lectures page for lecture video links.

To provide an option for students advised to or deciding to quarantine or social distance, lectures will be available live via Zoom. The zoom link is available via Canvas. Students should be able to ask questions either through chat or voice.

Instructor: Vivek Srikumar

Teaching Assistants

Course objectives

This course covers techniques for developing computer programs that can acquire new knowledge automatically or adapt their behavior over time. Topics include several algorithms for supervised and unsupervised learning, decision trees, online learning, linear classifiers, empirical risk minimization, computational learning theory, ensemble methods, Bayesian methods, and neural networks.

Expected learning outcomes: By the end of the semester, we hope that you will have:

1. A broad theoretical and practical understanding of machine learning paradigms and algorithms,

2. The ability to implement learning algorithms,

3. The ability to identify where machine learning can be applied and make the most appropriate decisions (about algorithms, models, supervision, etc).

Prerequisites

Students are expected to be familiar with:

1. Basic probability theory and statistics

2. Linear algebra

3. Enough computer science background to be able to understand and reason about algorithms and implement them

Of course, we will introduce some of the relevant prerequisite concepts in the lectures as needed, but knowing the topics will help in understanding the new material. The resources page lists references that may help you get up to speed on these topics.

We strongly prefer that you use python for the programming assignments. We will make exceptions to this language policy with permission.

Officially, the following prerequisites are enforced for undergraduate students:

1. You should be a full major in the data science program,

2. You should have obtained a C or better in CS 3500 and DS 3190.

Grading

The grades for the course will be based on 6-7 assignments (some of which will be quizzes), one final exam and a project. The different components will be weighted as follows:

Undergraduate and graduate students will be separately curved. Some assignments and the exam may include extra problems only for the graduate students.

Your assignments must be submitted electronically on Canvas by midnight of the due date. Detailed instructions for submission will accompany each assignment. Hand written assignments or printouts will not be accepted or graded.

See the homeworks page for information about submitting programming assignments.

Late policy

All assignments must be submitted by the deadline. We will use the timestamp on Canvas as the submission time. Assignments will be accepted up to 24 hours after deadline, but will be assessed a 10% penalty. That is, if your assignment is late and scores 90, then your actual grade will be 81 = 90 - 9.

Assignments will not be accepted 24 hours after the deadline.

We will be strict about this policy: If the deadline is midnight and you submit the assignment at 12:01 AM, you will face the 10% penalty! This may sound harsh, but we have to draw a line somewhere.

To get the best grades possible, we offer the following advice:

• You can submit files as often as you like, so always try to submit something before the due date!

• If you discover a major bug or finish solving a problem within 24 hrs after the due date, and you believe that your new solution is substantially improved over your original solution, then resubmit new files! In this case, you will be assessed the 10% late penalty. But your new solution could earn you a better score, so even with the late penalty you will end up with a higher grade.

Exceptions: All submissions are subject to the late day policy stated here. We understand, however, that certain factors may occasionally interfere with your ability to hand in work on time. If that factor is an extenuating circumstance such as a medical condition, we ask you to provide documentation directly issued by the University, and we will try to work out an agreeable solution with you.

No double dipping projects across multiple classes

You can not submit the same project to this class and another class that you may be taking at the same time. If you are doing related projects in two different classes, there may be some overlap (e.g. in code libraries, etc.), but they should not be identical. A project that is found to be double-submitted will receive zero credit. If you have questions about this policy, please contact the instructor.

Course Policies

Kahlert School of Computing Policies and Guidelines

The class operates under the School of Computing’s policies and guidelines. In particular, we will adhere to the school’s academic misconduct policy.

Also see the College of Engineering guidelines for information about appeals procedures, withdrawal procedures, and adding and repeating courses.

Collaboration and Cheating

We encourage collaborate; we will note tolerate cheating. Please do not cheat. It is not worth it for you, and it is unfair to your peers.

The Kahlert School of Computing has instituted a “two strikes and you’re out” policy. A strike occurs when you are reported for a major cheating (leading to failing a course), or two comparatively minor cheating instances. If you accumulate two strikes in any KSoC courses, you will be unable to register for any future KSoC courses. See this document for more details.

It is expected that students comply with University of Utah policies regarding academic honesty, including but not limited to refraining from cheating, plagiarizing, misrepresenting one’s work, and/or inappropriately collaborating. This includes the use of generative artificial intelligence (AI) tools without citation, documentation, or authorization. Students are expected to adhere to the prescribed professional and ethical standards of the profession/discipline for which they are preparing. Any student who engages in academic dishonesty or who violates the professional and ethical standards for their profession/discipline may be subject to academic sanctions as per the University of Utah’s Student Code: Policy 6-410: Student Academic Performance, Academic Conduct, and Professional and Ethical Conduct.

Plagiarism and cheating are serious offenses and may be punished by failure on an individual assignment, and/or failure in the course. Academic misconduct, according to the University of Utah Student Code:

“…Includes, but is not limited to, cheating, misrepresenting one’s work, inappropriately collaborating, plagiarism, and fabrication or falsification of information…It also includes facilitating academic misconduct by intentionally helping or attempting to help another to commit an act of academic misconduct.”

For details on plagiarism and other important course conduct issues, see the U’s Code of Student Rights and Responsibilities.

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 answers to assignment questions from other students, any online sources or large language models. Do not let someone else copy your submissions either. Both copying and sharing assignments will count as cheating.

If you are caught cheating once, you will receive a failing grade for that submission and receive a minor sanction. For repeated or systematic cheating, you will fail the class and receive a major sanction.

For the project, you are free to discuss the project with your classmates, but your work should be your own.

For both assignments and the project, you should cite all sources that you refer to. This includes personal communication, books, papers, websites, etc. Doing so reflects academic integrity.

For the exams, of course, we will allow neither collaboration nor cheating!

Student support

Students with Disabilities

The University of Utah seeks to provide equal access to its programs, services, and activities for people with disabilities.

All written information in this course can be made available in an alternative format with prior notification to the Center for Disability & Access (CDA). CDA 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, or for more information about what support they provide, contact:

University Safety Statement

The University of Utah values the safety of all campus community members. You will receive important emergency alerts and safety messages regarding campus safety via text message. For more safety information and to view available training resources, including helpful videos, visit safeu.utah.edu.

To report suspicious activity or to request a courtesy escort, contact:

Addressing Sexual Misconduct

Title IX makes it clear that violence and harassment based on sex and gender (which includes sexual orientation and gender identity/expression) is a civil rights offense subject to the same kinds of accountability and the same kinds of support applied to offenses against other protected categories such as race, national origin, color, religion, age, status as a person with a disability, veteran’s status, or genetic information.

If you or someone you know has been harassed or assaulted, you are encouraged to report it to university officials:

To file a police report, contact:

If you do not feel comfortable reporting to authorities, the U’s Victim-Survivor Advocates provide free, confidential, and trauma-informed support services to students, faculty, and staff who have experienced interpersonal violence.

To privately explore options and resources available to you with an advocate, contact:

Communication with Teaching Staff and Getting help

Don’t be shy if you don’t understand something: come to office hours, send email, or ask questions in class!

We strongly encourage you to post your questions on Piazza (access via canvas), unless you have a question that you wish to keep confidential. The instructor and the TAs will monitor the forum and will answer questions. Other students may also be able to answer questions. Do not post homework answers on the discussion board.

For grading questions, project related issues or simply a more hands-on interaction, we encourage you to attend the office hours or meet the instructor/TAs after class.

Of course, for personal/private questions, do feel free contact the instructor or the TAs by email or in person.

Please note that discussion threads and emails are all considered to be equivalent to the classroom, and your behavior in all these venues should conform to the university’s student code.

Textbooks and resources

See the resources page