ISM6251: Machine Learning for Business Applications

Fall 2025 Syllabus

Author

Dr. Tim Smith

Course Information

Course Title: Machine Learning for Business Applications
Course Number: ISM6251
Credit Hours: 3

Instructor Contact Information

Instructor: Dr. Tim Smith
Office: School of Information Systems and Management (CIS Building)
Email: smith515@usf.edu
Office Hours: Wednesdays 4:00 to 5:30 pm (or by appointment)

Teaching Assistant: Santhoshini Bojanapally
TA Email: bs441@usf.edu
TA Office Hours: Saturdays 1:00 to 4:00 pm

Important

Email is the best way to contact me, and I will try to answer emails within 24 business hours. For all communications, you must include the course number and section in the title of your email. Also, clarify your question/request – vagueness will result in wasting time with back-and-forth emails. Not including such details will result in your email being ignored.

Course Description

This course provides a hands-on introduction to machine learning techniques with a focus on practical business applications. Students will learn how to frame problems, prepare data, build and evaluate models, and communicate results to business stakeholders. The course emphasizes practical implementation using Python and covers various machine learning algorithms and model development processes, including regression, classification, clustering, and neural networks.

Course Learning Outcomes

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

Understand key machine learning concepts and their relevance to business decision-making
Apply regression and classification algorithms using Python and scikit-learn
Prepare and clean data using appropriate statistical and programmatic techniques
Evaluate and compare models using appropriate performance metrics
Identify business risks associated with overfitting, class imbalance, and interpretability
Present and communicate machine learning insights to both technical and non-technical audiences
Implement basic unsupervised learning approaches such as clustering and dimensionality reduction
Set up a development environment for machine learning projects
Develop and tune machine learning models using hyperparameter optimization techniques
Understand the principles of MLOps, including model deployment, monitoring, and governance

Prerequisites

Students are expected to have completed a course in Data Mining or have equivalent experience. Proficiency in Python programming is required, as Python will be the primary language used in this course. Additionally, familiarity with statistics, linear algebra, and basic data preprocessing techniques is recommended.

Course Policies

First-Day Attendance Policy

Per university policy, a student who is absent on the first day of class will automatically be dropped. Attendance will be taken at the start of class on the first day of class.

Attending the Class Virtually or Physically

Note that the schedule indicates if the class for that week is F2F (face-to-face) or AO (asynchronous online). AO classes are pre-recorded, and attendance is virtual. F2F classes will be live classes that you attend.

Note

The selective use of AO classes is to enhance student learning – as there are a small number of course topics that are more easily digested via a pre-recorded tutorial-style video, which allows the students to pause, repeat, and engage in complex material in a way that is more easily digested.

Course Materials

Students do not need to purchase any software or book for this class. However, the instructor may direct students to various online materials, books, manuals, videos, and software to supplement the class lecture.

How to Succeed in This Course

Access course regularly
Watch all recorded videos
Read all mandatory reading materials
Practice the examples and assignments – this is very important!
Work effectively in a team environment

Weekly Schedule

Week	Topics	Activities & Deliverables
1	Course Introduction & ML Foundations Course overview, expectations, and objectives Introduction to ML & Predictive Modeling (Regressors and Classifiers) What is machine learning? Business applications	- Slides: Course Introduction - Slides: ML Introduction - Discussion: Introduce yourself and ML aspirations - Quiz: Intro concepts
2	Python Fundamentals for ML Python programming essentials NumPy for numerical computing Pandas for data manipulation Matplotlib for data visualization	- Notebooks: Python, NumPy, Pandas, Matplotlib fundamentals - Assignment: Retail Sales Analysis (80 points) - Discussion: Role of programming in ML
3	Data Preparation Deep Dive Handling missing data, filtering, grouping Imputing vs. Dropping based on % missing by row/column	- Assignment: Data wrangling and imputation logic - Discussion: Tradeoffs in missing data strategies
4	Linear Regression Regression evaluation (MSE, R²) Data prep: Outlier detection and treatment	- Assignment: Build and evaluate linear models - Case: Revenue prediction scenario
5	Logistic Regression Binary classification Confusion Matrix, Precision, Recall, F1 Data prep: Encoding categorical variables	- Assignment: Classify customer churn - Discussion: Classification risk in business
6	K-Nearest Neighbors (KNN) Multi-class classification Evaluation: Multiclass confusion matrix Data prep: Feature scaling (standardization, normalization)	- Assignment: Product recommendation tiers - Case: Marketing segmentation
7	Support Vector Machines (SVC/SVM) Evaluation: Precision-Recall Curve Data prep: Class imbalance (resampling, weighting strategies)	- Assignment: Classify fraud vs. non-fraud - Discussion: Business risk vs. false positives
8	Decision Trees & Hyperparameter Tuning Evaluation: ROC and AUC Model complexity and interpretability	- Assignment: Credit scoring with tree ensembles - Case: Risk classification in finance
9	Ensemble Methods Random Forests, Gradient Boosting, XGBoost Feature importance and model selection	- Assignment: Advanced prediction models - Discussion: Combining models for business value
10	Artificial Neural Networks (ANNs) Evaluation with SHAP and feature importance Trade-offs in model complexity	- Assignment: Customer behavior modeling - Discussion: Interpreting black-box models in business
11	Clustering Techniques K-Means, DBSCAN, Hierarchical Evaluation: Silhouette Score, Davies-Bouldin	- Assignment: Customer segmentation analysis - Case: Campaign design using clusters
12	Recap and Final Exam Course review and comprehensive assessment across all topics	- Exam: Closed book, open notes (online) - Course evaluations - Final Project Due

Warning

NOTE: Course Schedule and Syllabus is Subject to Change at the Discretion of the Professor

Grading

Grading Components

Component	Weight
Weekly Assignments	15%
Discussion Participation	15%
In-Class Quizzes	20%
Final Project	25%
Final Exam	25%
Data Camp Courses	5% (bonus)

The students will be given an “Incomplete” grade only as per the university policy without any exception.

Grading Scale

Score	Grade
≥97	A+
≥94	A
≥90	A-
≥87	B+
≥84	B
≥80	B-
≥77	C+
≥74	C
≥70	C-
≥67	D+
≥64	D
≥60	D-
Below 60%	F

Note

Please note that this tentative grading scale can change based on the students and overall class performance. The faculty reserves the right to change the grading scale as he deems appropriate based on the class’s overall performance.

Assessment and Participation Policies

Quizzes

Quizzes will be conducted in-class using Honorlock
Formats include multiple choice, multiple select, fill-in-the-blank, and short answer
Quizzes are closed book and closed internet
Students are allowed to drop one lowest quiz score, typically to accommodate technical issues
- Only one dropped quiz is permitted
- Students must ensure Honorlock is working properly before quizzes; additional issues beyond the one drop will result in a zero

Final Exam

Administered in-class
Closed book, closed internet
No external assistance allowed, including AI tools

AI Policy

AI tools (e.g., ChatGPT) are encouraged as tutors or learning aids
Students may use AI to:
- Explore concepts and alternative explanations
- Brainstorm or clarify assignment directions
However, students must turn in original work and demonstrate understanding
AI may not be used during quizzes or the final exam

Technology and Equipment

Students must bring a fully charged laptop to every class
Power outlets are limited, and students are responsible for ensuring their laptop can last the entire session
If a student’s laptop fails or loses charge, it is considered their responsibility

Projects

Mid-Term Group Project: Analytics Project

The mid-term project is a group project. You will be split into groups of 4-5 students. As a group, you will build a model. At the end of the group project, each team member will evaluate the performance of their other team members. This is called a peer evaluation. Your actual mark on the final project will be the team’s mark multiplied by your percentage peer evaluation. For instance, let’s say your team received 100%, but your peer evaluation was 9/10. This means that your mark for the final project will be 90%.

Final Group Project: Big Data Analytics Project

The final project will be a group project. You will be split into groups of 4-5 students. As a group, you are to identify a publicly available dataset and do the analysis, visualization, and model creation on the dataset using Python. More details will be provided during the semester. At the end of the group project, each team member will evaluate the performance of their other team members. This is called a peer evaluation. Your actual mark on the final project will be the team’s mark multiplied by your percentage peer evaluation.

Course Policies

Late Work Policy

Important

No credit will be given to late submissions. It’s important that you plan your time well. You should target to complete the submission at least one day before the deadline. This is just good practice (and will help you in a professional setting in the future), as it will save you in embarrassing situations such as – your computer wasn’t working, your internet wasn’t working, you had last-minute issues, etc. Such excuses will not play well in a professional setting and, therefore, will not be acceptable in this class.

Grades of “Incomplete”

An “I” grade may be awarded to a student when:

Arrangements are made before the end of the semester
In the judgment of the instructor, a valid reason is offered for granting an Incomplete
A clear path to a standard grade is agreed to by the instructor and the student which will result in the successful completion of course requirements by the end of the succeeding semester

“I” grades not removed by the end of the next semester will be changed to “IF.”

Computer Requirements

You should have your own laptop (either MacOS or Windows) with at least 8 GB RAM to do the work needed in the class. If your laptop does not meet these requirements, you must notify the professor immediately.

Email

The primary means of communication between the instructor and students will be email. The instructor will occasionally send announcements to all students via Canvas. Students can feel free to email their instructor with questions at any time. Be sure to communicate clearly: What course, what section, and what is your question? Please anticipate a response time of 24 hours to email queries.

Canvas

This course will use Canvas to distribute materials, quizzes, tests, and midterm and final work. If you need help learning how to perform various tasks related to this course or other courses offered in Canvas, please consult the Canvas help guides. If you have any technical issues, contact USF’s IT department at (813) 974-1222 or help@usf.edu.

Class Videos

Florida law allows audio and/or video recordings of lectures only for personal use. Such recordings should not include audio or video of other students in the class and should not be publicly available and distributable.

Academic Integrity

Academic integrity is the foundation of the University of South Florida System’s commitment to the academic honesty and personal integrity of its university community. Academic integrity is grounded in certain fundamental values, which include honesty, respect, and fairness. Broadly defined, academic honesty is the completion of all academic endeavors and claims of scholarly knowledge as representative of one’s own efforts. The final decision on an academic integrity violation and related academic sanction at any USF System institution shall affect and be applied to the student’s academic status throughout the USF System unless otherwise determined by the independently accredited institution.

Disruption to Academic Process

Disruptive students in the academic setting hinder the educational process. Disruption of the academic process is defined as the act, words, or general conduct of a student in a classroom or other academic environment which, in the reasonable estimation of the instructor:

1. directs attention away from the academic matters at hand, such as noisy distractions, persistent, disrespectful or abusive interruption of lecture, exam, academic discussion, or general University operations, or
1. presents a danger to the health, safety, or well-being of self or other persons

Student Academic Grievance Procedures

These procedures provide all undergraduate and graduate students taking courses within the University of South Florida System an opportunity to objectively review facts and events pertinent to the cause of the academic grievance. An “academic grievance” is a claim that a specific academic decision or action that affects that student’s academic record or status has violated published policies and procedures or been applied to the grievant differently from that used for other students.

Standard University Policies

Policies about disability access, religious observances, academic grievances, academic integrity and misconduct, academic continuity, food insecurity, and sexual harassment are governed by a central set of policies that apply to all classes at USF. These may be accessed at: https://www.usf.edu/provost/faculty/core-syllabus-policy-statements.aspx

Disability Access

Students with disabilities are responsible for registering with Student Accessibility Services (SAS) to receive academic accommodations. You can visit the SAS website for additional information about academic accommodations and resources.

Religious Observances

All students have a right to expect that the University will reasonably accommodate their religious observances, practices, and beliefs. If you observe religious holidays, you should plan your allowed absences to include those dates.

Sexual Misconduct/Sexual Harassment Reporting

USF is committed to providing an environment free from sex discrimination, including sexual harassment and sexual violence (USF System Policy 0-004). The USF Center for Victim Advocacy and Violence Prevention is a confidential resource where you can talk about incidents of sexual harassment and gender-based crimes, including sexual assault, stalking, and domestic/relationship violence. This confidential resource can help you without having to report your situation to either the Office of Student Rights and Responsibilities (OSSR) or the Office of Diversity, Inclusion, and Equal Opportunity (DIEO), unless you request that they make a report. Please be aware that in compliance with Title IX and under the USF System Policy, educators must report incidents of sexual harassment and gender-based crimes, including sexual assault, stalking, and domestic/relationship violence. If you disclose any of these situations in class, in papers, or to me personally, I am required to report it to OSSR or DIEO for investigation. Contact the USF Center for Victim Advocacy and Violence Prevention: (813) 974-5757.

Additional Policies

Covid-19 Procedures

All students must comply with university policies and posted signs regarding COVID-19 mitigation measures (https://www.usf.edu/coronavirus/).

Additional details are available on the University’s Core Syllabus Policy Statements page: https://www.usf.edu/provost/faculty/core-syllabus-policy-statements.aspx

Online Proctoring

Quizzes and exams will be conducted using online proctoring tools. Keeping the audio and video (microphone and camera) on during such exams and quizzes is necessary. If the student is unwilling to use these, the student is asked not to register for this course. Any student may elect to drop or withdraw from this course before the end of the drop/add period. Online exams and quizzes within this course may require online proctoring. Therefore, students must have a webcam (USB or internal) with a microphone when taking an exam or quiz. Students understand that this remote recording device is purchased and controlled by the student and that recordings from any private residence must be done with any person’s permission. To avoid concerns, students should select private spaces for testing or in certain cases, the classroom. The University library and other academic sites at the University offer secure private settings for recordings, and students with concerns may discuss the location of an appropriate space for the recordings with their instructor or advisor. Students must ensure that recordings do not invade any third-party privacy rights and accept all responsibility and liability for violations of third-party privacy concerns. Setup information will be provided before taking the proctored exam. For additional information about online proctoring, you can visit the online proctoring student FAQ at https://www.usf.edu/innovative-education/student-resources/index.aspx

Turnitin.com

In this course, turnitin.com may be utilized. Turnitin is an automated system that instructors may use to quickly and easily compare each student’s assignment with billions of websites and an enormous database of student papers that grows with each submission. Accordingly, you will be expected to submit all assignments in both hard copy and electronic format. After the assignment is processed, as an instructor, I receive a report from turnitin.com that states if and how another author’s work was used in the assignment. For a more detailed look at this process, visit http://www.turnitin.com. Essays are due at turnitin.com the same day as in class.

Netiquette Guidelines

Act professionally in the way you communicate. Treat your instructors and peers respectfully like you would in a face-to-face environment. Respect other people’s ideas and be constructive when explaining your views about points you may disagree with.
Be sensitive. Be respectful and sensitive when sharing your ideas and opinions. People in your class will have different linguistic backgrounds, political and religious beliefs, or other general differences.
Proofread and check spelling. Doing this before sending an email or posting a thread on a discussion board will allow you to make sure your message is clear and thoughtful. Avoid using all capital letters, it can be perceived as if you are shouting, and it is more difficult to read.
Keep your communications focused and stay on topic. Complete your ideas before changing the subject. By keeping the message on focus, you allow the readers to easily get your idea or answers they are looking for.
Be clear with your message. Avoid using humor or sarcasm. Since people can’t see your expressions or hear your tone of voice, meaning can be misinterpreted.

Email and Discussion Board Guidelines

Use the subject line effectively using a meaningful line in your email or discussion.
Keep your emails and postings related to the course content. Unless the instructor requests, you should not post anything personal on a discussion board.
Any personal, course or confidential issues should be emailed to the instructor. The discussion boards are public spaces; therefore, personal issues should not be posted there.
Posts and discussions unrelated to class content are prohibited. A warning will be given for such a posting. Repeat offenders will lose access to the online class materials and thus risk failing the class.

End of Semester Student Evaluations

All classes at USF use an online system for students to provide feedback to the University regarding the course. These surveys will be made available at the end of the semester, and the University will notify you by email when the response window opens. Your participation is highly encouraged and valued.

WhatsApp, GroupMe, and Student-to-Student Communication

While students may use digital communication tools (WhatsApp, GroupMe, etc.) to communicate with fellow students, it is important to remember that academic integrity policies still apply in these environments. Informing others about the contents of tests is prohibited by the official regulation, as is receiving unauthorized information about an examination. Students are expected and required to report instances of such violations to the instructor immediately.

Title IX Policy

Title IX provides federal protections for discrimination based on sex, which includes discrimination based on pregnancy, sexual harassment, and interpersonal violence. In an effort to provide support and equal access, USF has designated all faculty (TA, Adjunct, etc.) as Responsible Employees who are required to report any disclosures of sexual harassment, sexual violence, relationship violence, or stalking. The Title IX Office makes every effort, when safe to do so, to reach out and provide resources and accommodation and to discuss possible options for resolution. Anyone wishing to make a Title IX report or seeking accommodations may do so online, in person, via phone, or email to the Title IX Office. For information about Title IX or for a full list of resources, please visit: https://www.usf.edu/titleix/gethelp/resources.aspx. If you are unsure what to do, please contact Victim Advocacy – a confidential resource that can review all your options – at 813-974-5756 or va@admin.usf.edu.

Course Hero / Chegg Policy

The USF Policy on Academic Integrity specifies that students may not use websites that enable cheating, such as by uploading or downloading material for this purpose. This does apply specifically to Chegg.com and CourseHero.com – almost any use of these websites (including uploading proprietary materials) constitutes a violation of the academic integrity policy.

Academic Accommodations

Academic Support Services

The USF Office of Student Success coordinates and promotes university-wide efforts to enhance undergraduate and graduate student success. Please visit the Office of Student Success website for a comprehensive list of academic support services available to all USF students.

Canvas Technical Support

If you have technical difficulties in Canvas, you can find access to the Canvas guides and video resources in the “Canvas Help” page on the homepage of your Canvas course. You can also contact the help desk by calling 813-974-1222 in Tampa or emailing help@usf.edu.

IT website for the Tampa campus
IT website for the St. Pete campus
IT website for the Sarasota-Manatee campus

Center for Victim Advocacy

The Center for Victim Advocacy empowers survivors of crime, violence, or abuse by promoting the restoration of decision making, by advocating for their rights, and by offering support and resources. Contact information is available online.

Counseling Center

The Counseling Center promotes the wellbeing of the campus community by providing culturally sensitive counseling, consultation, prevention, and training that enhances student academic and personal success. Contact information is available online.

Counseling Center website for the Tampa campus
Counseling Center website for the St. Pete campus
Counseling Center website for the Sarasota-Manatee campus

Campus Free Expression

It is fundamental to the University of South Florida’s mission to support an environment where divergent ideas, theories, and philosophies can be openly exchanged and critically evaluated. Consistent with these principles, this course may involve discussion of ideas that you find uncomfortable, disagreeable, or even offensive.

In the instructional setting, ideas are intended to be presented in an objective manner and not as an endorsement of what you should personally believe. Objective means that the idea(s) presented can be tested by critical peer review and rigorous debate and that the idea(s) is supported by credible research. Not all ideas can be supported by objective methods or criteria. Regardless, you may decide that certain ideas are worthy of your personal belief. In this course, however, you may be asked to engage with complex ideas and to demonstrate an understanding of the ideas. Understanding an idea does not mean that you are required to believe it or agree with it.

USF Core Syllabus Policies

USF has a set of central policies related to student recording class sessions, academic integrity and grievances, student accessibility services, academic disruption, religious observances, academic continuity, food insecurity, and sexual harassment that apply to all courses at USF. Be sure to review these online at: https://www.usf.edu/provost/faculty/core-syllabus-policy-statements.aspx

Any USF policy will supersede any policy mentioned in this syllabus.