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CS 4320: Machine Learning

Spring 2024 Syllabus

Course Description

For students pursuing degrees in Computer Science or related fields, with an interest in the theory and practice of machine learning. Covers an introduction to supervised and unsupervised learning, including decision trees, neural networks, naive Bayes classifiers and support vector machines. Students will be required to implement machine learning systems.

Prerequisites

CS 2420, CS 2810 and CS 3005 all with a C- or better.

Course fees

Course fee: $20, used to assist in maintaining department infrastructure.

Disability Statement

If you suspect or are aware that you have a disability that may affect your success in the course you are strongly encouraged to contact the Disability Resource Center (DRC) located in the North Plaza Building. The disability will be evaluated and eligible students will receive assistance in obtaining reasonable accommodations. Phone # 435-652-7516.

Sections

1. TR 9:00 - 10:15 pm in Smith 116

   Final exam April 30 at 9:00 am - 10:50 am

Instructor

Instructor: Curtis Larsen

Email: curtis.larsen@utahtech.edu

Phone: 435-652-7972

Office: Holland Centennial Commons 463

Office Hours:

• Mondays 9:30 am - 11:30 am
• Tuesdays 3:00 pm - 4:00 pm
• Wednesdays 1:00 pm - 2:00 pm
• Thursdays 3:00 pm - 4:00 pm

Office hours are a fantastic opportunity to receive personalized assistance with any questions or concerns you may have about course concepts, assignments, or anything else on your mind.

I’m here to support you throughout the week with regular office hours. No need to worry about telling me in advance to use the scheduled office hours, just drop by whenever it suits you! Whether you prefer a face-to-face chat in my office at HCC 463 or joining me virtually via Zoom, the choice is yours. You can find the Zoom link conveniently posted in an announcement on Canvas.

If these times don’t fit your schedule, don’t hesitate to reach out to me. Simply shoot me an email or a message through Canvas, and we can find a time that works better for you. When you message me, suggesting a few alternative times would be incredibly helpful.

Looking forward to connecting with you during office hours!

Student Learning Outcomes

The student will be able to discuss the principles of:

• supervised learning,
• unsupervised learning,
• decision trees,
• artificial neural networks,
• genetic algorithms,
• Bayesian learning,
• support vector machines.

The student will be able to:

• use supervised and unsupervised learning techniques,
• implement software learning systems,
• evaluate quality of learned systems,
• implement software utilizing the results of learning systems.

Resources

Textbook

There is no required textbook for this course. The following book is offered as an optional resource.

1. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron, ISBN-13: 978-1-492-03264-9

The following texts may be of interest as well.

1. Online References NumPy Matplotlib Matplotlib Tutorials Pandas Pandas Tutorials scikit-learn TensorFlow Keras

2. Artificial Intelligence: A Modern Approach 3rd Ed. by Russell and Norvig, ISBN: 978-0-13-604259-4 (optional)

3. Artificial Intelligence: Structures and Strategies for Complex Problem Solving (6th Edition) by George F. Luger ISBN: 978-0321545893 (optional)

4. Machine Learning by Tom M. Mitchell ISBN: 978-0070428072 (optional)

5. Artificial Intelligence Subsequent Edition by Elaine Rich and Kevin Knight ISBN: 978-0070522633 (optional)

6. Foundations of Machine Learning by Mohri, Rostamizadeh, Talwalkar ISBN: 978-0262018258 (optional)

7. Artificial Intelligence: Foundations of Computational Agents, 2nd Edition by David L. Poole and Alan K. Mackworth Read Online ISBN: 9781107195394 (optional)

8. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy ISBN: 978-0262018029 (optional)

9. Artificial Intelligence in the 21st Century 2nd Edition by Stephen Lucci and Danny Kopec ISBN: 978-1942270003 (optional)

10. Bayesian Reasoning and Machine Learning by David Barber ISBN: 978-0521518147 (optional)

Other Resources

• You will need access to one or more computer systems with Python3, numpy, matplotlib, pandas, and tensorflow installed.

Computer Labs

You may use departmental computers and software in the Smith Computer Center. Some lab assistants may be able to help with assignments and pass off homework assignments for introductory courses.

Course Web Site

Assignment submissions and grades will be managed in the Canvas System.

Assignments and Exams

Reading

While there is no required text book. Students are expected to find and read relevant references.

Assignments

A series of assignments will be required. These assignments are due on the date and time listed in the schedule, or as stated by the instructor during class. It cannot be over emphasized that it is important to start early and get all of your assignments done before their due dates. Programs that do not run correctly will receive no credit. Most assignments will require a written report.

There will be a final project at the end of the semester.

Participation

You will be expected to give positive contributions to class discussions.

Presentations

There will be a required presentation at the end of the semester. You will present your project and its results for the rest of the class.

Grading

Letter grades will be assigned based on the percentage of total possible points attained. The point totals will be approximately: assignments and final project 150. The following chart will be used for letter grades.

Course Policies

Attendance

Students are responsible for material covered and announcements made in class. School-related absences may be made up only if prior arrangements are made. The class schedule presented is approximate. The instructor reserves the right to modify the schedule according to class needs. Changes will be announced in class and posted to the website. Exams and quizzes cannot be made up unless arrangements are made prior to the scheduled time.

Occasional absences are acceptable as long as the student keeps up with assignment work. Students who miss more than two consecutive weeks of class or who miss more than 20% of scheduled classes during the semester without making prior arrangements will receive a failing grade. Students who miss any scheduled exam (including midterm exams and the final exam) or fail to complete a final project without making prior arrangements will receive a failing grade.

Time Commitment

Courses should require about 45 hours of work per credit hour of class. This class will require about 135 hours of work on the part of the student to achieve a passing grade, which is approximately 9 hours per week. If you do not have the time to spend on this course, you should probably rethink your schedule.

Late Policy

Each assignment has two due dates. The earliest due date is the required date. The second date is the absolute latest date to submit the assignment. Late work will not be accepted after the second date.

Collaboration

Limited collaboration with other students in the course is permitted. Students may seek help learning concepts and developing programming skills from whatever sources they have available, and are encouraged to do so. Collaboration on assignments, however, must be confined to course instructors, lab assistants, and other students in the course. Students are free to discuss strategies for solving programming assignments with each other, but this must not extend to the level of programming code. Each student must code his/her own solution to each assignment. See the section on cheating.

Cheating

Cheating will not be tolerated, and will result in a failing grade for the students involved as well as possible disciplinary action from the college. Cheating includes, but is not limited to, turning in homework assignments that are not the student’s own work. It is okay to seek help from others and from reference materials, but only if you learn the material. As a general rule, if you cannot delete your assignment, start over, and re-create it successfully without further help, then your homework is not considered your own work.

You are encouraged to work in groups while studying for tests, discussing class lectures, discussing algorithms for homework solutions, and helping each other identify errors in your homework solutions. If you are unsure if collaboration is appropriate, contact the instructor. Also, note exactly what you did. If your actions are determined to be inappropriate, the response will be much more favorable if you are honest and complete in your disclosure.

Where collaboration is permitted, each student must still create and type in his/her own solution. Any kind of copying and pasting is not okay. If you need help understanding concepts, get it from the instructor or fellow classmates, but never copy another’s code or written work, either electronically or visually. The line between collaborating and cheating is generally one of language: talking about solutions in English or other natural languages is usually okay, while discussions that take place in programming languages are usually not okay. It is a good idea to wait at least 30 minutes after any discussion to start your independent write-up. This will help you commit what you have learned to long-term memory as well as help to avoid crossing the line to cheating.

University Policies

Code of Student Rights and Responsibilities (Academic dishonesty / academic integrity policy, student academic conduct policy)

Financial Aid

Registration

Student Association

Student absence related to college function

Sexual Harassment

Disability/Accessibility Resources

Utah Tech welcomes all students and strives to make the learning experience accessible. If you are a student with a medical, psychological, or learning disability that may require accommodations for this course, you are encouraged to contact the Disability Resource Center (DRC) as soon as possible. You may request reasonable accommodations at any time during the semester; however, they are not retroactive. The DRC is located next door to the Testing Center in the North Commons Building. (435-652-7516, drc@utahtech.edu).

Title IX Statement

Utah Tech University affirms its commitment to the promotion of fairness in all aspects of the educational institution. Harassment and discrimination – including sex/gender discrimination, gender identity, gender expression, sexual harassment, sexual misconduct, gender-based violence, dating violence, domestic violence, stalking, pregnancy or parental , family or marital status and or retaliation –not only disrupts our commitment to maintaining an environment in which every member of the University community is treated with respect and dignity, but may also violate University policy and federal, state, and/or local law. Should you or someone you know experience behavior that is coercive, discriminatory, harassing, and or sexually violent in nature, or if you or someone you know has questions about their rights and options regarding such behavior, you are encouraged to contact:

   Hazel Sainsbury
   Dir. Of Equity Compliance, Title IX Coordinator
   435.652.7747 (ext. 7747)
   hazel.sainsbury@utahtech.edu
   titleix@utahtech.edu

Incidents may also be reported directly to law enforcement, either separately or in conjunction with any report made to the University’s Title IX Coordinator, and the University will aid in making contact if requested.

      Utah Tech University Police
      435.275.4300 or by calling 9-1-1

Maintaining a safe and inclusive University community is a shared responsibility. For more information on how Title IX protections can benefit you and help us keep a productive campus environment, visit titleix.utahtech.edu to learn more.

Email Disclaimer

You are required to frequently check your Utah Tech email account as important class and university information will be sent to this account, including bills, financial aid/scholarship notices, notices of canceled classes, reminders of important dates and deadlines, course information, and other information critical to your success at UT. To access your Utah Tech email account, visit mail.utahtech.edu. Your email account username is Digital-ID@utahtech.edu (e.g. D12345678@utahtech.edu). If you don’t know or have forgotten your Digital-ID or password, please visit changepassword.utahtech.edu.

Useful Resources

• Disability Resource Center
• IT Help Desk
• Library
• Testing Center
• Tutoring Center
• Writing Center

Important Dates Fall 2025

Click on this link for important dates: https://catalog.utahtech.edu/academiccalendar

Aug 20 - Classes Begin

Aug 26 – Last day to add w/out instructor signature

Aug 29 – $10 drop/audit fee begins ($10 per class)

Sept 1 Labor Day Holiday (no classes)

Sept 2 - Residency application deadline, $100 late registration/payment fee

Sept 10 – Last day for refund; Last day to drop w/out a W, Pell Grant Census

Sept 17 – Last day to add/audit any course w instructor signature

Oct 9-Oct 10 - Semester Break (no classes)

Oct 13 - Midterm grades due

Oct 17 - Last day to drop an individual class

Nov 14 - Last day for complete withdrawal from all classes

Nov 24-Nov 28 - Thanksgiving Break (no classes)

Dec 5 - Classes End

Dec 8 - Dec 11 - Final Exams

Dec 15 Final grades due by instructors

Last Updated 01/08/2024