ISM6251: Machine Learning for Business Applications

Building Data-Driven Solutions for Tomorrow's Business Challenges

📧 Dr. Tim Smith 🏫 Fall 2025 🎓 USF School of Information Systems and Management

📚 Course Documents

📋 Course Syllabus Required

Complete course information including schedule, grading policies, and learning objectives

Grading Information Weekly Schedule Course Policies Contact Information

🗺️ Course Structure Overview

Complete Course Structure Analysis

A comprehensive overview of all course topics with hierarchical organization and critical analysis of the curriculum structure.

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📅 Weekly Course Materials

Week 1: Course Introduction & ML Overview
Introduction to Machine Learning & Predictive Modeling • Python & Jupyter Notebooks Setup

🎯 Part A: Course Introduction

Course overview, expectations, and logistics

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🤖 Part B: Introduction to ML

What is ML, types, business applications

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💬 Discussion Forum

Introduce yourself and share ML aspirations

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Week 2: Python Fundamentals for ML
Python basics • NumPy arrays • Pandas DataFrames • Matplotlib visualization

🐍 Python Fundamentals

Complete Python, NumPy, Pandas, and Matplotlib tutorial

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📓 Notebook A: Python Basics

Variables, data structures, functions, and modules

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📓 Notebook B: NumPy

Arrays, operations, broadcasting, and vectorization

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📓 Notebook C: Pandas

DataFrames, data selection, and manipulation

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📓 Notebook D: Matplotlib

Scatter plots, histograms, and bar charts

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💬 Discussion Forum

Building Your Python Data Science Foundation

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📝 Assignment 1

Retail Sales Analysis with Python & Pandas

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Week 3: Data Preparation Deep Dive
Data quality assessment • Missing data strategies • Outlier detection • Filtering & grouping • Preparation pipelines

📊 Data Preparation

Comprehensive data cleaning and preparation techniques

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📓 Notebook A: Data Quality

Data quality assessment and missing data strategies

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📓 Notebook B: Filtering & Grouping

Advanced filtering techniques and aggregation operations

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📓 Notebook C: Data Pipelines

Building production-ready data preparation pipelines

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📝 Week 3 Assignment

Customer Data Pipeline: From Chaos to Clarity

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📊 Assignment Notebook

Starter notebook for data preparation assignment

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🔧 Data Generator

Script to generate your unique dataset

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Week 4: Linear Regression
Regression evaluation (MSE, R²) • Outlier detection and treatment
Materials will be available soon
Week 5: Logistic Regression
Binary classification • Confusion Matrix, Precision, Recall, F1 • Encoding categorical variables
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Week 6: K-Nearest Neighbors (KNN)
Multi-class classification • Feature scaling (standardization, normalization)
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Week 7: Support Vector Machines
SVC/SVM • Precision-Recall Curve • Class imbalance strategies
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Week 8: Decision Trees & Hyperparameter Tuning
ROC and AUC • Model complexity and interpretability
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Week 9: Ensemble Methods
Random Forests, Gradient Boosting, XGBoost • Feature importance
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Week 10: Artificial Neural Networks
ANNs • SHAP and feature importance • Model complexity trade-offs
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Week 11: Clustering Techniques
K-Means, DBSCAN, Hierarchical • Silhouette Score, Davies-Bouldin
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Week 12: Course Review & Final Exam
Comprehensive review • Final project presentations
Materials will be available soon