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, inclusion and wellness
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 significantly 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 fro 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 computer science and the computer engineering program,

2. You should have obtained a C- or better in CS 3500 and CS/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

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, and it is unfair to your peers.

The 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 SoC courses, you will be unable to register for any future SoC courses. See this document for more details.

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 or any online sources. 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, inclusion and wellness

Students with Disabilities

The University of Utah seeks to provide equal access to its programs, services and activities for people with disabilities. If you will need accommodations in the class, reasonable prior notice needs to be given to the Center for Disability and Access, 162 Olpin Union Building, 581-5020. CDA will work with you and the instructor to make arrangements for accommodations. Accommodations cannot be given without paperwork from this office.

University Safety Statement

The University of Utah values the safety of all campus community members. To report suspicious activity or to request a courtesy escort, call campus police at 801-585-COPS (801-585-2677). You will receive important emergency alerts and safety messages regarding campus safety via text message. For more information regarding safety and to view available training resources, including helpful videos, visit safeu.utah.edu.

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, are harassed or assaulted, you are encouraged to report it to the Title IX Coordinator in the Office of Equal Opportunity and Affirmative Action, 135 Park Building, 801-581-8365, or the Office of the Dean of Students, 270 Union Building, 801-581-7066. For support and confidential consultation, contact the Center for Student Wellness, 426 SSB, 801-581-7776. To report to the police, contact the Department of Public Safety, 801-585-2677(COPS).

Student names and personal pronouns

Class rosters are provided to the instructor with the students legal name as well as Preferred first name (if previously entered by you in the Student Profile section of your CIS account, which managed can be managed at any time). While CIS refers to this as merely a preference, I will honor you by referring to you with the name and pronoun that feels best for you in class or on assignments.

Please advise me of any name or pronoun changes so I can help create a learning environment in which you, your name, and your pronoun are respected. If you need any assistance or support, please reach out to the LGBT Resource Center

Wellness

Personal concerns such as stress, anxiety, relationship difficulties, depression, cross-cultural differences, etc. can interfere with a student’s ability to succeed and thrive at the University of Utah.

For helpful resources, contact the Center for Student Wellness online, or on the phone at 801-581-7776.

Contact the instructor if there is any additional support that would aid in this course.

Diversity

It is our intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit. It is our intent to present materials and activities that are respectful of diversity: gender identity, sexuality, disability, age, socioeconomic status, ethnicity, race, nationality, religion, and culture. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally, or for other students or student groups.

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 the class discussion board on 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 on Canvas. 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