Course materials, Python code, assignments, and project work for ISM 6642, Section RG6 at Florida International University.
| Item | Details |
|---|---|
| Term | Fall 2026 |
| Format | Hybrid/HyFlex |
| Classroom | RB 151 |
| Instructor | Hemang Subramanian |
| hsubrama@fiu.edu | |
| Phone | (305) 348-8446 |
| Office | RB 250 |
| Office hours | Thursday and Friday, 11:00 a.m.-1:00 p.m. |
| Department | Information Systems and Business Analytics (ISBA) |
The complete course document is available in the official syllabus. Canvas remains the source of truth for announcements, assignment instructions, due dates, and schedule changes.
This course develops an applied understanding of machine learning for business. Python is the primary analytical language. The course connects machine-learning techniques to business problems while addressing the benefits and challenges of integrating models into business operations.
No prerequisite or required textbook is specified in the syllabus.
By the end of the course, students should be able to:
- Use Python libraries and tools that support machine-learning workflows.
- Identify, explain, and implement machine-learning algorithms for business problems.
- Prepare, preprocess, and transform business data for modeling.
- Design, train, and evaluate models for practical business use cases.
- Compare model performance and tune models for accuracy and efficiency.
- Collaborate effectively on team-based analytical projects.
| Week | Date | Topics and milestones |
|---|---|---|
| 1 | Aug. 22 | Course, project, and software overview; business statistics and machine learning; software setup; basic linear regression; enroll in a Canvas group. |
| 2 | Aug. 29 | Logistic regression, variable selection, and LASSO (online); conditional logistic regression/choice models (TBD); in-class assignment. |
| 3 | Sept. 5 (online) | K-means clustering; choosing K; midterm practice; take-home assignment on linear and logistic regression. |
| 4 | Sept. 12 | Hierarchical clustering, including DIANA and AGNES; in-class Midterm Exam 1; clustering homework. |
| 5 | Sept. 19 | Neural-network classification; recurrent neural networks (RNNs); long short-term memory (LSTM); convolutional neural networks (CNNs); in-class assignment. |
| 6 | Sept. 26 (online) | Take-home exam. |
| 7 | Oct. 3 | Introduction to large language models; the Agrawal (2017) paper; natural language processing; data dictionaries and NLTK; scikit-learn and Python libraries; bag-of-words models; LLM tokenization; in-class assignment. |
| 8 | Oct. 10 | Final project presentation; optional short exam covering course topics. |
The instructor may modify this schedule with notice to students.
Individual and group assignments are described in Canvas. Unless otherwise stated, assignment submissions should include:
- A report explaining the process and steps taken.
- Model development details and results.
- Model evaluations and conclusions.
- The corresponding Python code uploaded to Canvas.
The group project requires students to select a relevant dataset and apply course methods to a meaningful business decision. Deliverables include:
- A short presentation.
- A professional Microsoft Word report describing the data, models, results, model comparisons, and conclusions.
- The group's Python code uploaded to Canvas.
One project submission per group is sufficient. A peer evaluation may be used to adjust individual project and assignment grades based on each member's contribution.
The syllabus lists the following point structure:
| Requirement | Number of items | Points each | Total shown |
|---|---|---|---|
| In-class participation and individual/group take-home assignments | Each class session | 100 | 600 |
| Midterms 1 and 2 | 2 | 100 | 200 |
| Final (group project and test) | 1 | 100 | 100 |
| Total row in syllabus | 6 | N/A | 600 |
Note
The listed component totals add to 900 points, while the syllabus total row states 600 points. Confirm the final point structure with the instructor or in Canvas.
| Grade | Range | Grade | Range | Grade | Range |
|---|---|---|---|---|---|
| A | 95 or above | B | 83-86 | C | 70-76 |
| A- | 90-94 | B- | 80-82 | D | 60-69 |
| B+ | 87-89 | C+ | 77-79 | F | 59 or less |
- Attend every scheduled in-person session and arrive on time. Attendance is recorded at the beginning of class, and late arrival may result in an absence or attendance penalty.
- Missing more than one in-person class session results in a full letter-grade reduction in the final course grade, regardless of reason, according to the syllabus.
- Submit original work, do not assist others in cheating, and follow FIU's Student Code of Academic Integrity.
- Review the Policies & Resources page in Canvas for university policies, accommodations, online etiquette, technology requirements, privacy and accessibility statements, academic integrity, copyright, nondiscrimination, and student support resources.
Week 1/- introductory lecture materials, a peer-programming lab, presentation files, and Python code.Week 1/README.md- instructions for the descriptive-statistics peer-programming lab.Week 1/business_review.py- the Week 1 Python exercise.
This README is a practical summary of the syllabus. When it differs from an instructor announcement or Canvas, follow the most recent course guidance.