Comparison with Traditional Programming

Differences Between Traditional Programming and ML-Based Approaches:

• Traditional Programming:
  • Approach: The programmer provides explicit instructions to the computer to perform a task. The logic and rules are predefined by the programmer based on a clear understanding of the problem.
  • Handling Complexity: Well-suited for tasks with deterministic rules and fixed logic. Complex scenarios require extensive coding and maintenance.
  • Data Dependency: Does not inherently depend on large amounts of data; the focus is on the logic rather than data.
• Machine Learning:
  • Approach: The algorithm learns patterns and relationships from data. Instead of giving explicit instructions, data is provided, and the model discovers the underlying patterns itself.
  • Handling Complexity: Well-suited for tasks where patterns are not easily defined or are too complex to be manually coded. Models can adapt to new data without manual intervention.
  • Data Dependency: Highly dependent on large datasets for training. The performance improves as the amount of diverse and representative data increases.

Use Cases Where Each Approach is Most Effective:

• Traditional Programming:
  • Use Case: Calculating payroll, where the rules for tax deduction are fixed and can be explicitly coded.
• Machine Learning:
  • Use Case: Image recognition, where defining explicit rules for identifying objects in various contexts would be infeasible.

Source:

• Machine Learning by Tom M. Mitchell
• Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani