Three Applications Where ML is Appropriate (pattern-based, large data, no explicit rule):

1. Handwritten Digit/ Face Recognition: Too many variations to code rules; learn from examples (MNIST, CNN). Data abundant, pattern statistical.
2. Spam Email Filtering / Recommendation Systems: User preferences shift, need adaptation from labeled feedback; probabilistic text features ideal for Naive Bayes / collaborative filtering.
3. Speech Recognition / Machine Translation / Medical Diagnosis from images: No exact algorithmic solution, benefits from deep learning on large datasets (e.g., tumor detection from X-rays).

Three Where ML is Inappropriate (deterministic, exact, safety-critical, small data):

1. Simple Arithmetic / Payroll Calculation / Sorting: Exact algorithmic solution exists, 100% accuracy required; ML would approximate and err.
2. Real-time Safety-Critical Control with Formal Guarantees (e.g., Aircraft flight control certification, Nuclear reactor shutdown): Need provable correctness, interpretability; ML black-box unreliable, hard to verify.
3. Problems with No Data / No Pattern (e.g., determining a cryptographic hash inverse, generating truly random numbers, or one-off legal reasoning requiring causal explanation): ML needs historical data; if data absent or task requires deductive reasoning/common sense not statistical correlation, ML fails.

Criteria: ML appropriate when: large data, pattern exists but not analytically describable, tolerance for probabilistic error, adaptation needed. Inappropriate when: exact solution exists, need guarantee, no data, or solution requires reasoning beyond correlation.