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Southern Utah University
Southern Utah University
Fall Semester 2025

Machine Learning (Face-to-Face)

CS 4150-01

Course: CS 4150-01
Credits: 3
Term: Fall Semester 2025
Department: CSIS
CRN: 33607

Course Description

COURSE OVERVIEW

Prerequisites: CS 2420 - Introduction to Algorithms and Data Structures (Minimum Grade: C)

Course Description: This course is designed to foster practical machine learning expertise, encompassing theoretical foundations spanning various machine learning principles and techniques. It emphasizes hands-on learning through programming assignments and project implementations, enabling students to apply their theoretical knowledge in real-world scenarios. Ultimately, the course aims to equip participants with a comprehensive skill set in machine learning, blending theory with practical application.

Required Texts

Required Textbook/Materials

  • Deep Learning by Ian Goodfellow, Yoshoua Bengio, and Aaron Courville
  • Python Machine Learning by Sebastian Raschka
Supporting optional materials
  • Pattern Recognition and Machine Learning by Christopher M. Bishop

Also: Course Notes and Handouts

Course Materials

  • Computer
  • Reliable Internet connection and software
  • Access to Canvas

Learning Outcomes

Course Learning Outcomes

  • Comprehend a wide variety of real time Machine Learning (ML) based Neural Networks (NN) and Deep Learning (DL) concepts.
  • Understand the Perceptron in ML based Deep Learning techniques.
  • Understand Gradient Descent in fitting of neurons for ML.
  • Understand and implement Clustering and Cloud Computing in ML.
  • Understand and implement Logistic Regression, Multi Class Classifications, Multilayer Perceptron and regularizations in ML.
  • Understand and implement Learning Rate and Optimization algorithms in ML.
  • Understand and implement Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, and Auto-Encoder.
  • Real time research project hands on experience on ML implementation.

Course Requirements

Grading Scale

94 - 100 | A
90 - 93 | A-
87 - 89 | B+
84 - 86 | B
80 - 83 | B-
77 - 79 | C+
74 - 76 | C
70 - 73 | C-
67 - 69 | D+
64 - 66 | D
61 - 63 | D-
Below 61 | F

Grading Plan

Weekly Assignments | 20%
Weekly Quizzes | 10%
Tests | 20%
Projects | 30%
Final Exam | 20%
Total | 100%

Major assignment and examination
    1. Assignments 20%
        Assignment 1: Install Python, Keras, Tensorflow
        Assignment 2: On building Neural Networks in Python Code
        Assignment 3: Neural Networks, Multi Layer Perceptron, Regression, Classifications.
        Assignment 4: Radial Basis Functions (RBFs)
        Assignment 5: Clustering, Finding Mean Centroid
        Assignment 6: RBFN (Radial Basis Functions Networks)
        Assignment 7: Dimentionality Reduction

     2. Quizzes 10%
        Quiz 1: On Artificial Intelligence and Agents
        Quiz 2: Linear Algebra, Matrix Operations, BFS and DFS, Stochastic vs. Deterministic Models.
        Quiz 3: On building Neural Networks, Classification Techniques

     3. Tests 20%
        Test 1.
        Test 2.

     4. Projects 30%
        Project Proposal.
        Literature Review.
        Project Presentation 1.
        Final Project Presentation and 10-12 page IEEE Conference Standard Project Report Submission.

     5. Final Exam 20%

Course Outline

Course Schedule

Note: Schedule subject to change. If any modification or changes, you will be notified through Canvas. Additional reading/activities will be assigned as needed.

WeekTopicOutcomes/StandardsActivitiesAssignments
1Introduction to Machine Learning and Deep Learning, an overview. (Ch-1 from textbook 1, Ch-1 from textbook 2)Brief Discussion on Project.Formation of the Project Group (Max 4-5 persons/group) - Name of Project Members (According to Last Name)
2A brief summary of Neural Network (NN) and Deep Learning (DL) (Ch-1 from textbook 1, Ch-2 from textbook 2)Understanding the history of NN and DLQ&A and Student Feedback. Discussion on Project Proposal in details.Quiz 1
3The Perceptron (Ch-2 from textbook 1, Ch-2 from textbook 2)Understanding of PerceptronProject Proposal Due (2 pages Max, 12 fonts 1.5-line space). A guideline of project proposal should be available on the Canvas/classroom. Assignment 1 will be posted.
4Linear Algebra (LA) for DL (Ch-2 from textbook 1, Ch-2 from textbook 2)Understanding of LA in details required for DL.Q&A in the Class and Online.Quiz 3
5Gradient Descent in NN. (Ch-4 from textbook 1)Understanding of Gradient Descent in details for neurons.Q&A in the Class and Online.Quiz 4
6Clustering. (Ch-11 from textbook 2)Understanding of Clustering.Assignment 1 due. Assignment 2 will be posted.Quiz 5
7Cloud Computing. (Online Materials will be provided)Understanding of Cloud Computing in ML.Q&A in Class and Online.Quiz 6
8Logistic Regression (LR) and Multi-class Classification (MCC). (Ch-7 from textbook 1, Ch-10, Ch-12 from textbook 2)Understanding of LR and MCC.Assignment 2 due.
9Multilayer Perceptron (MLP) Test 1 Review (Ch-7 from textbook 1, Ch-12 from textbook 2)Understand MLP.Q&A in Class and Online.Test 1
10Learning Rates (LR) and Optimization Algorithms (OA) (Ch-5 from textbook 1, Ch-6 from textbook 2)Understand LR and OA.Assignment 3 will be posted.Quiz 7
11Convolutional Neural Networks (CNNs) Test 2 Review (Ch-9 from textbook 1, Ch-15 from textbook 2)Understand CNNs.Project update 2 with Deliverables.
12Generative Adversarial Networks (GANs) (Ch-20 from textbook 1, Ch-17 from textbook 2)Understand GANs.Assignment 3 due.Test 2
13Recurrent Neural Networks (RNNs) (Ch-10 from textbook 1, Ch-16 from textbook 2)Understand RNNs.Q&A in Class and Online.Project Presentation
14Auto Encoder (AE) and Variational Auto Encoder (VAE) (Ch-14 from textbook 1)Understand AE and VAE.Q&A in Class and Online.Final Exam Review
15Final ExamFinal ExamDec 8, ELC 30611:00am – 12:50pm

Instructor's policies on late assignments and/or makeup work

Assignment Submission Policy

All assignments and discussions for this course will be submitted electronically through Canvas unless otherwise instructed. Assignments and discussions must be submitted by the given deadline or special permission must be requested from instructor before the due date. Extensions will not be given beyond the next assignment except under extreme circumstances. Late or missing assignments and discussions will affect the student’s grade.

Late Work

  • Assignments and quizzes are due when specified.
  • Assignments will be accepted up to 3 days after their specified due date with a 20 percent deduction or at the discretion of the instructor.
  • Three days after the specified due dates, access to assignments and quizzes will be restricted.
  • If a student will be unable to complete an assignment or quiz by the due date, he/she will need to make prior arrangements with the instructor.
  • This restriction is lifted for the first week of classes due to students signing into the class late.

Attendance Policy

Attendance and Participation Requirements

  • Class attendance is mandatory for face-to-face classes.
  • Students may miss class only for authorized reasons (athletic events, academic travel, special ceremonies, etc.)
  • Illness and personal emergencies may also cause students to be absent for legitimate reasons.
  • Should a student miss class for any reason, he/she will make every reasonable effort to notify the professor in advance of the absence with evidence.
  • The student will be responsible for any material covered in class.
  • If a student knows he/she will be absent for an examination or on the due date of a major graded requirement, that student will coordinate with his/her instructor for completion/submission requirements.
  • Active participation in class discussions and activities is an expectation of this course.

Course Fees

Fees

All CSIS classes have a per credit hour student fee. These fees go toward funding mediated classes, computer labs, software upgrades and licenses, printer paper and toner, and other student-related costs.

Custom

Instructor Information

Instructor: Prosenjit Chatterjee, PhD
Class Time: 11:30am – 12:45pm ELC306
Office: ELC 414
Phone: 435-865-8399 (office)
Office Hours: TBD (appointments by email recommended 24 hours before the meeting)
Email: prosenjitchatterjee@suu.edu

Course Resources

Canvas / Southern Utah University Approved Learning Management System: Over the course of the semester, reading notes, lesson plans, discussion prompts, self-guided lectures, grades, and other administrative information will be posted on Canvas. Students must check Canvas regularly.

For technical support:
#435-865-8200
support@suu.edu
IT Service Desk: https://www.suu.edu/it/

For Canvas help:
435-865-8555
canvas@suu.edu
Canvas Help Center: https://help.suu.edu/canvas/
How to Use Canvas

Communication

  • In this course we will use Canvas to send emails to your Canvas email account. Please check your messages regularly.
  • Assignments will not be accepted via email.
  • Check your SUU email frequently and at least once daily.

Acceptable and Unacceptable Use of AI

The use of generative AI tools (e.g., ChatGPT, Dall-e, etc.) is permitted for:

  • Brainstorming and refining your ideas.
  • Finding information on your topic (verify that information).
  • Checking grammar and style.

Not permitted for:

  • Impersonating you in classroom contexts.
  • Completing individual or group work assigned to you.
  • Writing drafts or complete sentences, paragraphs, or papers for assignments.

Improper use will result in a zero. Proper citation is required.

Academic Integrity

Scholastic dishonesty will not be tolerated and will be prosecuted to the fullest extent under Policy 6.33. Violations may result in a 0 on an assignment or an F in the course and notification of University officials.

ADA Statement

Students with disabilities seeking accommodations should contact SSD in Room 206G, Sharwan Smith Center or call (435) 865-8022.

Emergency Management Statement

In case of emergency, the ENS will be activated. Maintain updated contact information via mySUU. Familiarize yourself with Emergency Response Protocols: suu.edu/ad/facilities/emergency-procedures.html.

HEOA Compliance Statement

Sharing copyrighted material via P2P file sharing, except as provided under U.S. copyright law, is prohibited. More info: suu.edu/it/p2p-student-notice.html

Disclaimer & Continuity of Instruction

Information in this syllabus, other than grading, late assignments, makeup work, and attendance policies, may change with advance notice. During suspended face-to-face instruction, check SUU email and Canvas for instructions.

Online Course Requirements

  • Computer
  • Reliable Internet connection and software (DSL, LAN, or cable connection desirable)
  • Access to Canvas
  • Webcam
  • Scanning capability (possibly with smartphone)

SUU Campus Resources and Services

Review SUU Campus Resources and Services: campus-resources-and-services.pdf and the Student Handbook: help.suu.edu/handbook. Vendor resources on accessibility and privacy: vendor-resources.html

Financial Security Statement

If you are struggling financially, visit the Financial Wellness Center (financialwellness) in room 201C Sharwan Smith Center, contact Ashleigh Zimmerman at (435) 865-8436, or text 435-708-1952.

General Health Practice Statement

Model vaccination, daily health self-assessment, testing, and hygiene habits:

  • Vaccination
  • Health monitoring
  • Coronavirus testing
  • Hygiene habits

Hygiene Habits

  • Wash your hands frequently for at least 20 seconds or use an alcohol-based sanitizer.
  • Don't touch your face.
  • Cover coughs and sneezes with your elbow or upper arm.
  • Stay home if you are sick and seek medical guidance.
  • Practice social distancing (stay at least six feet away).
  • Wear a mask indoors when recommended.

ADA Statement

Students with medical, psychological, learning, or other disabilities desiring academic adjustments, accommodations, or auxiliary aids will need to contact the Disability Resource Center, located in Room 206F of the Sharwan Smith Center or by phone at (435) 865-8042. The Disability Resource Center determines eligibility for and authorizes the provision of services.

If your instructor requires attendance, you may need to seek an ADA accommodation to request an exception to this attendance policy. Please contact the Disability Resource Center to determine what, if any, ADA accommodations are reasonable and appropriate.

Academic Credit

According to the federal definition of a Carnegie credit hour: A credit hour of work is the equivalent of approximately 60 minutes of class time or independent study work. A minimum of 45 hours of work by each student is required for each unit of credit. Credit is earned only when course requirements are met. One (1) credit hour is equivalent to 15 contact hours of lecture, discussion, testing, evaluation, or seminar, as well as 30 hours of student homework. An equivalent amount of work is expected for laboratory work, internships, practica, studio, and other academic work leading to the awarding of credit hours. Credit granted for individual courses, labs, or studio classes ranges from 0.5 to 15 credit hours per semester.

Academic Freedom

SUU is operated for the common good of the greater community it serves. The common good depends upon the free search for truth and its free exposition. Academic Freedom is the right of faculty to study, discuss, investigate, teach, and publish. Academic Freedom is essential to these purposes and applies to both teaching and research.

Academic Freedom in the realm of teaching is fundamental for the protection of the rights of the faculty member and of you, the student, with respect to the free pursuit of learning and discovery. Faculty members possess the right to full freedom in the classroom in discussing their subjects. They may present any controversial material relevant to their courses and their intended learning outcomes, but they shall take care not to introduce into their teaching controversial materials which have no relation to the subject being taught or the intended learning outcomes for the course.

As such, students enrolled in any course at SUU may encounter topics, perspectives, and ideas that are unfamiliar or controversial, with the educational intent of providing a meaningful learning environment that fosters your growth and development. These parameters related to Academic Freedom are included in SUU Policy 6.6.

Academic Misconduct

Scholastic honesty is expected of all students. Dishonesty will not be tolerated and will be prosecuted to the fullest extent (see SUU Policy 6.33). You are expected to have read and understood the current SUU student conduct code (SUU Policy 11.2) regarding student responsibilities and rights, the intellectual property policy (SUU Policy 5.52), information about procedures, and what constitutes acceptable behavior.

Please Note: The use of websites or services that sell essays is a violation of these policies; likewise, the use of websites or services that provide answers to assignments, quizzes, or tests is also a violation of these policies. Regarding the use of Generative Artificial Intelligence (AI), you should check with your individual course instructor.

Emergency Management Statement

In case of an emergency, the University's Emergency Notification System (ENS) will be activated. Students are encouraged to maintain updated contact information using the link on the homepage of the mySUU portal. In addition, students are encouraged to familiarize themselves with the Emergency Response Protocols posted in each classroom. Detailed information about the University's emergency management plan can be found at https://www.suu.edu/emergency.

HEOA Compliance Statement

For a full set of Higher Education Opportunity Act (HEOA) compliance statements, please visit https://www.suu.edu/heoa. The sharing of copyrighted material through peer-to-peer (P2P) file sharing, except as provided under U.S. copyright law, is prohibited by law; additional information can be found at https://my.suu.edu/help/article/1096/heoa-compliance-plan.

You are also expected to comply with policies regarding intellectual property (SUU Policy 5.52) and copyright (SUU Policy 5.54).

Mandatory Reporting

University policy (SUU Policy 5.60) requires instructors to report disclosures received from students that indicate they have been subjected to sexual misconduct/harassment. The University defines sexual harassment consistent with Federal Regulations (34 C.F.R. Part 106, Subpart D) to include quid pro quo, hostile environment harassment, sexual assault, dating violence, domestic violence, and stalking. When students communicate this information to an instructor in-person, by email, or within writing assignments, the instructor will report that to the Title IX Coordinator to ensure students receive support from the Title IX Office. A reporting form is available at https://cm.maxient.com/reportingform.php?SouthernUtahUniv

Non-Discrimination Statement

SUU is committed to fostering an inclusive community of lifelong learners and believes our university's encompassing of different views, beliefs, and identities makes us stronger, more innovative, and better prepared for the global society.

SUU does not discriminate on the basis of race, religion, color, national origin, citizenship, sex (including sex discrimination and sexual harassment), sexual orientation, gender identity, age, ancestry, disability status, pregnancy, pregnancy-related conditions, genetic information, military status, veteran status, or other bases protected by applicable law in employment, treatment, admission, access to educational programs and activities, or other University benefits or services.

SUU strives to cultivate a campus environment that encourages freedom of expression from diverse viewpoints. We encourage all to dialogue within a spirit of respect, civility, and decency.

For additional information on non-discrimination, please see SUU Policy 5.27 and/or visit https://www.suu.edu/nondiscrimination.

Pregnancy

Students who are or become pregnant during this course may receive reasonable modifications to facilitate continued access and participation in the course. Pregnancy and related conditions are broadly defined to include pregnancy, childbirth, termination of pregnancy, lactation, related medical conditions, and recovery. To obtain reasonable modifications, please make a request to title9@suu.edu. To learn more visit: https://www.suu.edu/titleix/pregnancy.html.

Disclaimer Statement

Information contained in this syllabus, other than the grading, late assignments, makeup work, and attendance policies, may be subject to change with advance notice, as deemed appropriate by the instructor.