4  Machine Learning II

4.1 Multiple Linear Regression

4.2 Logistic Regression

4.2.1 Classification

4.3 Overfitting and Underfitting

  • Underfitting: model too simple to capture data patterns
  • Overfitting: model too complex, captures noise instead of signal
  • Validation helps detect these behaviors.

4.3.1 Unsupervised Learning Use Cases

Use Case Sample Inputs Model Output Description What ML Question is Being Answered? What Business Question is Being Answered? Example Algorithm(s)
Customer Segmentation Age, income, purchase history Cluster/group labels for each customer What types of customers exist in my data? How can I tailor marketing strategies to different customer types? K-means, DBSCAN
Topic Modeling Articles or documents Topics with keywords per document What topics are being discussed? What content themes resonate most with my audience or market? LDA, NMF
Anomaly Detection Transaction logs, sensor data Anomaly score or binary flag Which data points are unusual? Are there fraudulent transactions or system failures I need to act on? Isolation Forest, Autoencoder
Dimensionality Reduction High-dimensional features (e.g., pixels) 2D or 3D projections for analysis or visualization How can I reduce feature space while preserving info? How can I visualize or simplify complex data for human analysis or modeling? PCA, t-SNE, UMAP
Market Basket Analysis Sets of purchased items Association rules (A & B → C) What items co-occur frequently in purchases? Which product bundles or cross-sell offers should I promote? Apriori, FP-Growth
Word Embedding Text corpus Word vectors capturing semantic similarity What are the contextual relationships between words? How can I build a smarter search engine or chatbot that understands language context? Word2Vec, GloVe
Image Compression Raw pixel arrays Compressed version of the image How can I represent this image with fewer features? How can I reduce storage or transmission costs for image data? Autoencoders

4.4 Reinforcement Learning

4.4.1 Reinforcement Learning Use Cases

Use Case Sample Inputs Model Output Description What ML Question is Being Answered? What Business Question is Being Answered? Example Algorithm(s)
Game Playing Game state (e.g., board, score) Action to take What should I do to win the game? How can I build an AI that outperforms humans or creates adaptive gameplay? Q-learning, DQN
Robotics & Control Sensor data (angles, velocities, etc.) Movement or control signals How should the agent move next to reach a goal? How can I automate physical tasks like picking, sorting, or navigating? PPO, SAC, DDPG
Autonomous Vehicles Sensor input (camera, LIDAR, speed, GPS) Driving action What’s the optimal next driving move? How can I develop a safe and efficient self-driving vehicle system? Deep RL + sensor fusion
Recommendation Systems User history, preferences, session behavior Recommended item What should I recommend next? How can I increase user retention, engagement, or sales? Contextual Bandits, RL
Portfolio Management Financial indicators, stock prices Asset allocation decision How should I invest to maximize return? How can I build an automated trading or portfolio optimization system? Actor-Critic methods
Personalized Education Student progress and quiz results Next learning step What lesson or content should come next? How can I boost student outcomes by personalizing learning pathways? Multi-armed bandits
Healthcare Treatment Patient history and vitals Treatment or intervention strategy What care plan maximizes long-term patient health? How can I optimize healthcare outcomes while reducing costs and readmissions? Off-policy RL, POMDPs

4.5 Glossary

  • logistic (sigmoid) function:

  • Softmax: