ISM6251: Course Structure Overview

Hierarchical Topic Organization and Critical Analysis

Course Topic Hierarchies

This document presents the complete hierarchical structure of topics covered in ISM6251, followed by a critical analysis of the course organization.

Week 1a: Course Introduction

📚 Course Structure
├── Welcome & Overview
│   ├── Course objectives
│   ├── Learning outcomes
│   └── Why ML for business?
├── 12-Week Schedule
│   ├── Foundations (Weeks 1-4)
│   ├── Core ML (Weeks 5-8)
│   └── Advanced (Weeks 9-12)
└── Class Format
    ├── Interactive lectures
    ├── Hands-on coding
    └── Business cases

📊 Grading Components
├── Assignments (40%)
│   ├── Weekly programming
│   └── Business applications
├── Exams (30%)
│   ├── Midterm exam
│   └── Final exam
├── Projects (20%)
│   └── Real-world ML project
└── Participation (10%)
    └── Class engagement

🔧 Technology Requirements
├── Software Setup
│   ├── Python 3.8+
│   ├── Jupyter notebooks
│   └── VS Code/PyCharm
├── Libraries
│   ├── NumPy, Pandas
│   ├── Scikit-learn
│   └── TensorFlow/PyTorch
└── Platforms
    ├── GitHub classroom
    └── Canvas LMS

📋 Course Policies
├── Academic Integrity
│   ├── Code attribution
│   └── Collaboration rules
├── Late Work
│   └── 10% daily penalty
└── Resources
    ├── Office hours
    ├── TA support
    └── Study groups

🚀 Getting Started
├── Pre-requisites Check
│   ├── Basic programming
│   └── Statistics foundation
├── First Week Tasks
│   ├── Environment setup
│   └── Diagnostic quiz
└── Success Strategies
    ├── Active participation
    ├── Regular practice
    └── Business thinking

Week 1b: ML Introduction

📖 Core Concepts
├── What is Machine Learning?
│   ├── Evolution of Intelligent Systems
│   ├── Traditional vs ML Approach
│   └── Key Differentiators
├── Formal Definition
│   ├── Task (T) - What to solve
│   ├── Experience (E) - Learning data
│   └── Performance (P) - Success metrics
└── ML vs Traditional Programming
    ├── Rule-based → Data-driven
    ├── Explicit → Implicit logic
    └── Static → Adaptive systems

🎯 Types of Machine Learning
├── Supervised Learning
│   ├── Classification
│   │   ├── Binary classification
│   │   └── Multi-class problems
│   └── Regression
│       ├── Linear relationships
│       └── Non-linear patterns
├── Unsupervised Learning
│   ├── Clustering (K-means, DBSCAN)
│   └── Dimensionality Reduction (PCA)
└── Reinforcement Learning
    ├── Agent-Environment interaction
    ├── Reward optimization
    └── Q-learning, Policy gradient

⚙️ ML Workflow
├── 1. Problem Definition
│   ├── Business objectives
│   └── Success metrics
├── 2. Data Collection
│   ├── Source identification
│   └── Quality assessment
├── 3. Data Preparation
│   ├── Cleaning & validation
│   └── Feature engineering
├── 4. Model Selection
│   ├── Algorithm choice
│   └── Complexity tradeoffs
├── 5. Model Training
│   ├── Split strategies
│   ├── Hyperparameter tuning
│   └── Cross-validation
├── 6. Model Evaluation
│   ├── Classification metrics
│   └── Regression metrics
├── 7. Deployment
│   ├── API services
│   ├── Batch processing
│   └── Edge deployment
└── 8. Monitoring & Maintenance
    ├── Performance tracking
    ├── Data drift detection
    └── Model retraining

💼 Business Applications
├── Industry Sectors
│   ├── Finance (fraud, credit)
│   ├── Healthcare (diagnosis, drug)
│   ├── Retail (recommendations)
│   └── Manufacturing (quality, predictive)
└── Success Stories
    ├── Netflix recommendations
    ├── Amazon forecasting
    └── UPS route optimization

Week 2: Python Fundamentals

🐍 Python Basics
├── Environment Setup
│   ├── Python installation
│   ├── Package managers (pip, conda)
│   └── Virtual environments
├── Core Syntax
│   ├── Variables & data types
│   ├── Control flow (if, for, while)
│   └── Functions & scope
└── Data Structures
    ├── Lists & tuples
    ├── Dictionaries & sets
    └── List comprehensions

📊 NumPy Essentials
├── Array Operations
│   ├── Creating arrays
│   ├── Indexing & slicing
│   └── Broadcasting
├── Mathematical Functions
│   ├── Element-wise operations
│   ├── Linear algebra
│   └── Statistical functions
└── Performance
    ├── Vectorization benefits
    └── Memory efficiency

🐼 Pandas Fundamentals
├── DataFrames & Series
│   ├── Creating DataFrames
│   ├── Reading/writing files
│   └── Data selection
├── Data Manipulation
│   ├── Filtering & sorting
│   ├── GroupBy operations
│   └── Merging & joining
└── Time Series
    ├── DateTime handling
    └── Resampling

📈 Data Visualization
├── Matplotlib Basics
│   ├── Line plots
│   ├── Scatter plots
│   └── Histograms
├── Seaborn
│   ├── Statistical plots
│   ├── Heatmaps
│   └── Pair plots
└── Interactive Plots
    └── Plotly basics

🔧 Development Tools
├── Jupyter Notebooks
│   ├── Cell execution
│   ├── Markdown integration
│   └── Magic commands
├── Debugging
│   ├── Print debugging
│   ├── Python debugger
│   └── Error handling
└── Version Control
    ├── Git basics
    └── GitHub workflow

Week 3: Data Preparation

📊 Data Quality & Cleaning
├── Data Quality Assessment
│   ├── Completeness checks
│   ├── Consistency validation
│   └── Accuracy verification
├── Missing Data Handling
│   ├── Detection methods
│   ├── Imputation strategies
│   │   ├── Mean/median/mode
│   │   ├── Forward/backward fill
│   │   └── KNN imputation
│   └── Deletion approaches
└── Outlier Detection
    ├── Statistical methods (IQR, Z-score)
    ├── Visualization techniques
    └── Domain-based rules

🔄 Data Transformation
├── Scaling & Normalization
│   ├── Min-Max scaling
│   ├── Standardization (Z-score)
│   └── Robust scaling
├── Encoding Categorical Variables
│   ├── One-hot encoding
│   ├── Label encoding
│   ├── Target encoding
│   └── Dummy variable trap
└── Feature Engineering
    ├── Creating new features
    ├── Polynomial features
    └── Interaction terms

📈 Statistical Analysis
├── Descriptive Statistics
│   ├── Central tendency
│   ├── Dispersion measures
│   └── Distribution shape
├── Correlation Analysis
│   ├── Pearson correlation
│   ├── Spearman correlation
│   └── Correlation matrices
└── Statistical Tests
    ├── Normality tests
    ├── T-tests
    └── Chi-square tests

🎯 Feature Selection
├── Filter Methods
│   ├── Variance threshold
│   ├── Correlation threshold
│   └── Mutual information
├── Wrapper Methods
│   ├── Forward selection
│   ├── Backward elimination
│   └── Recursive feature elimination
└── Embedded Methods
    ├── LASSO (L1)
    ├── Ridge (L2)
    └── Tree-based importance

⚠️ Common Pitfalls
├── Data Leakage
│   └── Train-test contamination
├── Sampling Bias
│   └── Non-representative samples
└── Overfitting Features
    └── Too many engineered features

Week 4: Linear Regression

📐 Fundamentals
├── Simple Linear Regression
│   ├── y = mx + b
│   ├── Least squares method
│   └── Interpretation
├── Multiple Linear Regression
│   ├── Multiple predictors
│   ├── Matrix formulation
│   └── Coefficient meaning
└── Assumptions
    ├── Linearity
    ├── Independence
    ├── Homoscedasticity
    ├── Normality of residuals
    └── No multicollinearity

🔬 Model Building
├── Ordinary Least Squares (OLS)
│   ├── Normal equation
│   ├── Gradient descent
│   └── Computational complexity
├── Feature Interactions
│   ├── Cross-product terms
│   ├── Business interpretation
│   └── Connection to kernels
└── Polynomial Regression
    ├── Degree selection
    ├── Overfitting risks
    └── Regularization need

📊 Model Evaluation
├── Metrics
│   ├── R-squared
│   ├── Adjusted R-squared
│   ├── MSE/RMSE/MAE
│   └── MAPE
├── Residual Analysis
│   ├── Residual plots
│   ├── Q-Q plots
│   └── Pattern detection
└── Cross-Validation
    ├── K-fold CV
    ├── Leave-one-out
    └── Time series splits

🎯 Regularization
├── Ridge Regression (L2)
│   ├── Penalty term: λΣβ²
│   ├── Shrinkage effect
│   └── All features kept
├── LASSO (L1)
│   ├── Penalty term: λΣ|β|
│   ├── Feature selection
│   └── Sparse solutions
└── Elastic Net
    ├── Combined L1+L2
    ├── Best of both
    └── Hyperparameter tuning

💼 Business Applications
├── Sales Forecasting
├── Price Optimization
├── Risk Assessment
└── Demand Planning

Week 5: Classification & Logistic Regression

📊 Classification Fundamentals
├── Binary vs Multi-class
│   ├── Problem formulation
│   ├── Decision boundaries
│   └── Class imbalance
├── Linear Classifiers
│   ├── Perceptron
│   ├── Linear discriminant
│   └── Separability
└── Probabilistic View
    ├── Class probabilities
    ├── Decision thresholds
    └── Confidence scores

🎯 Logistic Regression
├── Core Concepts
│   ├── Sigmoid function: 1/(1+e^-z)
│   ├── Log-odds interpretation
│   └── Maximum likelihood
├── Model Training
│   ├── Gradient descent
│   ├── Newton-Raphson
│   └── Convergence criteria
├── Multi-class Extension
│   ├── One-vs-Rest (OvR)
│   ├── One-vs-One (OvO)
│   └── Softmax regression
└── Regularization
    ├── L1/L2 penalties
    ├── C parameter tuning
    └── Feature importance

📈 Model Evaluation
├── Confusion Matrix
│   ├── TP, TN, FP, FN
│   ├── Visual interpretation
│   └── Multi-class extensions
├── Key Metrics
│   ├── Accuracy limitations
│   ├── Precision & Recall
│   ├── F1-Score
│   └── Matthews Correlation
├── ROC Analysis
│   ├── ROC curve
│   ├── AUC interpretation
│   └── Threshold selection
└── Class Imbalance
    ├── SMOTE
    ├── Class weights
    └── Stratified sampling

⚙️ Advanced Topics
├── Feature Engineering
│   ├── Polynomial features
│   ├── Interaction terms
│   └── Domain features
├── Ensemble Methods
│   ├── Voting classifiers
│   ├── Bagging
│   └── Boosting preview
└── Calibration
    ├── Platt scaling
    ├── Isotonic regression
    └── Reliability diagrams

💼 Applications
├── Credit Scoring
├── Medical Diagnosis
├── Customer Churn
└── Fraud Detection

Week 6: KNN & Distance Metrics

🎯 KNN Fundamentals
├── Core Algorithm
│   ├── Instance-based learning
│   ├── Lazy vs eager learning
│   └── Non-parametric nature
├── Classification Process
│   ├── Distance calculation
│   ├── K nearest selection
│   └── Majority voting
└── Regression with KNN
    ├── Average of neighbors
    ├── Weighted average
    └── Local smoothing

📏 Distance Metrics
├── Minkowski Family
│   ├── Euclidean (p=2): √Σ(xi-yi)²
│   ├── Manhattan (p=1): Σ|xi-yi|
│   └── Chebyshev (p=∞): max|xi-yi|
├── Specialized Metrics
│   ├── Cosine similarity
│   ├── Hamming distance
│   └── Mahalanobis distance
└── Metric Selection
    ├── Data type considerations
    ├── Scale sensitivity
    └── Curse of dimensionality

⚙️ Implementation Details
├── K Selection
│   ├── Odd vs even K
│   ├── Cross-validation
│   └── Elbow method
├── Optimization Techniques
│   ├── KD-trees
│   ├── Ball trees
│   └── Approximate methods
└── Weighted KNN
    ├── Distance weighting
    ├── Kernel functions
    └── Adaptive K

🔧 Preprocessing for KNN
├── Feature Scaling
│   ├── Standardization critical
│   ├── Min-max normalization
│   └── Robust scaling
├── Dimensionality Reduction
│   ├── PCA preprocessing
│   ├── Feature selection
│   └── Manifold learning
└── Missing Values
    ├── Imputation strategies
    └── Distance modifications

📊 Evaluation & Applications
├── Performance Considerations
│   ├── Computational complexity
│   ├── Memory requirements
│   └── Prediction speed
├── Strengths & Weaknesses
│   ├── ✓ Simple, interpretable
│   ├── ✓ No training phase
│   ├── ✗ Curse of dimensionality
│   └── ✗ Sensitive to noise
└── Use Cases
    ├── Recommendation systems
    ├── Pattern recognition
    └── Anomaly detection

Week 7: Support Vector Machines

📐 Linear SVM
├── Maximum Margin Classifier
│   ├── Separating hyperplane
│   ├── Support vectors
│   └── Margin maximization
├── Mathematical Foundation
│   ├── Optimization problem
│   ├── Lagrange multipliers
│   └── KKT conditions
└── Soft Margin SVM
    ├── Slack variables (ξ)
    ├── C parameter tuning
    └── Error tolerance

🌀 Kernel Trick
├── Non-linear Transformation
│   ├── Feature space mapping φ(x)
│   ├── Implicit computation
│   └── Kernel functions K(x,y)
├── Common Kernels
│   ├── Linear: x·y
│   ├── Polynomial: (γx·y + r)^d
│   ├── RBF: exp(-γ||x-y||²)
│   └── Sigmoid: tanh(γx·y + r)
└── Kernel Selection
    ├── Data complexity
    ├── Computational cost
    └── Cross-validation

⚙️ Implementation
├── Training Process
│   ├── Quadratic programming
│   ├── SMO algorithm
│   └── LibSVM/LibLinear
├── Hyperparameters
│   ├── C: regularization
│   ├── γ: kernel coefficient
│   ├── degree: polynomial
│   └── Grid search
└── Multi-class SVM
    ├── One-vs-Rest
    ├── One-vs-One
    └── ECOC strategies

📊 Practical Aspects
├── Preprocessing
│   ├── Feature scaling crucial
│   ├── Outlier sensitivity
│   └── Dimensionality
├── Model Selection
│   ├── Cross-validation
│   ├── Nested CV
│   └── Performance metrics
└── Computational Considerations
    ├── O(n²) to O(n³) training
    ├── Memory requirements
    └── Sparse data handling

💼 Applications & Extensions
├── Text Classification
│   ├── Document categorization
│   └── Sentiment analysis
├── Image Recognition
│   ├── Face detection
│   └── Object classification
├── SVM Variants
│   ├── SVR (regression)
│   ├── One-class SVM
│   └── Structured SVM
└── Comparison with Others
    ├── vs Logistic Regression
    ├── vs Neural Networks
    └── vs Random Forests

Critical Analysis of Course Structure

Overall Assessment

The course demonstrates a well-thought-out progression from foundational concepts to advanced machine learning techniques. The structure follows a logical learning path that builds knowledge incrementally. However, there are several areas where the organization could be enhanced for better learning outcomes.

Strengths of Current Structure

1. Progressive Complexity

The course effectively builds from basic Python programming (Week 2) through data preparation (Week 3) to increasingly complex ML algorithms (Weeks 4-7). This scaffolding approach helps students develop confidence before tackling advanced topics.

2. Theory-Practice Balance

Each week appears to blend theoretical foundations with practical implementation, which is essential for business students who need both conceptual understanding and hands-on skills.

3. Business Context Integration

The consistent inclusion of business applications and use cases throughout each week helps students understand the practical relevance of technical concepts.

Areas for Improvement

1. Missing Critical Topics

Several important topics appear to be absent or underrepresented:

• Deep Learning Fundamentals: Given the 12-week structure mentioned in Week 1a, weeks 8-12 content is missing. This should include:
  • Neural network basics
  • Deep learning frameworks
  • CNNs for computer vision
  • RNNs/LSTMs for sequential data
  • Transfer learning
• Ensemble Methods: While briefly mentioned in Week 5, dedicated coverage is needed for:
  • Random Forests
  • Gradient Boosting (XGBoost, LightGBM)
  • Stacking and blending
• Model Deployment & MLOps: Critical for business applications but not adequately covered:
  • Model serialization
  • API development
  • Docker containers
  • Cloud deployment (AWS, Azure, GCP)
  • Model monitoring in production
• Time Series Analysis: Important for business forecasting but absent:
  • ARIMA models
  • Seasonal decomposition
  • Prophet and modern approaches

2. Sequencing Issues

• Feature Engineering Placement: Currently split between Week 3 (basic) and Week 4 (interactions). Consider consolidating or creating a dedicated module after students understand basic modeling.

• Evaluation Metrics Redundancy: Model evaluation appears in multiple weeks (4, 5, 6, 7). Consider a unified evaluation framework introduced early and referenced throughout.

• Statistical Foundations: Week 3 includes statistical analysis, but students might benefit from this earlier (perhaps Week 2) as it underlies all ML concepts.

3. Depth vs. Breadth Concerns

• Week 2 Scope: Attempting to cover Python basics, NumPy, Pandas, visualization, and development tools in one week seems ambitious. Consider splitting into two weeks or providing pre-course preparation.

• SVM Complexity: Week 7’s SVM content is quite theoretical (Lagrange multipliers, KKT conditions). For business students, consider emphasizing practical usage over mathematical derivations.

Recommended Restructuring

Option 1: Rebalanced 12-Week Structure

Weeks 1-2: Foundations
- Week 1: Course intro + ML overview (current 1a & 1b)
- Week 2: Python & statistical foundations (expanded)

Weeks 3-4: Data Preparation
- Week 3: Data quality, cleaning, and transformation
- Week 4: Feature engineering and selection (consolidated)

Weeks 5-8: Core ML Algorithms
- Week 5: Linear models (regression & logistic)
- Week 6: Tree-based methods (decision trees, Random Forest)
- Week 7: Ensemble methods (boosting, stacking)
- Week 8: Instance-based methods (KNN) & SVM

Weeks 9-11: Advanced Topics
- Week 9: Neural networks & deep learning basics
- Week 10: Unsupervised learning (clustering, PCA)
- Week 11: Time series & specialized applications

Week 12: Integration & Deployment
- Model deployment, MLOps, and course project presentations

Option 2: Module-Based Organization

Instead of weekly topics, organize into modules that can flex based on student progress:

Module 1: Foundations (3-4 weeks)
- Programming skills
- Statistical concepts
- Data manipulation
- ML overview

Module 2: Supervised Learning (4-5 weeks)
- Regression methods
- Classification algorithms
- Ensemble techniques
- Evaluation frameworks

Module 3: Advanced Topics (2-3 weeks)
- Unsupervised learning
- Deep learning introduction
- Special topics (NLP, computer vision basics)

Module 4: Application & Deployment (1-2 weeks)
- Real-world projects
- Deployment strategies
- Ethics and bias

Specific Recommendations

1. Create Prerequisites Module

Develop a pre-course module covering Python basics and statistical foundations, allowing Week 2 to focus on ML-specific libraries and techniques.

2. Integrate Evaluation Framework

Introduce a comprehensive evaluation framework in Week 3 that’s consistently applied throughout, rather than re-teaching metrics in each algorithm week.

3. Add Practical Workshops

Include dedicated workshop sessions for: - Kaggle competition walkthroughs - Real dataset challenges - Industry guest speakers - Deployment exercises

4. Enhance Business Integration

Each week should include: - A business case study - ROI calculations for ML projects - Stakeholder communication exercises - Ethical considerations

5. Include Modern Tools

Update tool coverage to include: - AutoML platforms (H2O, AutoSklearn) - Experiment tracking (MLflow, Weights & Biases) - Cloud ML services - Low-code ML platforms

6. Add Capstone Project Thread

Introduce a semester-long project that builds incrementally: - Week 2-3: Problem definition and data collection - Week 4-5: Initial modeling - Week 6-8: Model improvement - Week 9-11: Advanced techniques - Week 12: Deployment and presentation

Conclusion

The current course structure provides a solid foundation for machine learning education with good progressive complexity and business integration. However, to better serve business students in 2025, the course would benefit from:

1. More comprehensive coverage of modern ML topics (deep learning, MLOps)
2. Better sequencing to reduce redundancy and improve flow
3. Stronger emphasis on practical deployment and business value
4. Integration of contemporary tools and platforms
5. A clearer path from theory to production-ready applications

These improvements would transform the course from a traditional ML survey into a more practical, business-focused program that prepares students for real-world ML implementation and management.