Having examined specific data structures like trees, hash tables, and graphs used in machine learning, we now turn to the broader methods for solving computational problems. This chapter introduces core algorithmic strategies. These are general approaches or design patterns for constructing algorithms, which appear frequently within machine learning implementations.

You will be introduced to:

• Divide and Conquer: Breaking problems into smaller, self-similar subproblems.
• Dynamic Programming: Solving problems by breaking them into simpler overlapping subproblems and storing their solutions.
• Greedy Algorithms: Making locally optimal choices at each stage.
• Randomized Algorithms: Using randomness as part of the algorithm's logic, often affecting performance or output characteristics.
• Iterative Optimization: Viewing methods like gradient descent through an algorithmic lens.

Recognizing these strategies will help you understand the structure of common ML algorithms, analyze their performance characteristics, and appreciate the trade-offs involved in various library implementations.