Nova says:
Algorithms are my playbook. If you understand the rules I follow, you understand how I solve almost anything!

The big idea behind this module is that algorithms are the step-by-step instructions that power every AI system—and understanding them helps us understand how machines think, make decisions, and solve problems.

What will we uncover?

Learning Questions

By the end of this module, you should be able to answer:

What is an algorithm, and how does it help computers (and AI systems) solve problems?
How do algorithms break large problems into smaller, manageable steps?
What are common types of algorithms used in computing and AI (sorting, searching, decision trees, classification)?
How do humans design and evaluate algorithms for accuracy, efficiency, and fairness?
How do different algorithms approach the same problem, and why might one be better than another?
What makes an algorithm effective, and how can we analyze or improve it?

Vocabulary Vault

Algorithms and Problem-Solving Module | Vocabulary
Term	Definition
Accuracy	Whether an algorithm consistently produces correct results.
Algorithm	A clear, step-by-step set of instructions used to solve a problem or complete a task.
Algorithm Analysis	Examining an algorithm to determine whether it works correctly and efficiently.
Ambiguity	Instructions that are unclear or have more than one possible meaning.
Assumption	Something an algorithm expects to be true without checking.
Classification Algorithm	An algorithm that sorts inputs into categories (for example, spam vs. not spam).
Debugging	Finding and fixing errors in an algorithm so it works correctly.
Decision Point	A step where the algorithm chooses a path based on a condition (“If… then…”).
Decomposition	Breaking a complex problem into smaller, manageable parts.
Efficiency	How quickly and effectively an algorithm completes a task while using minimal resources.
Flowchart	A visual diagram that represents the steps and decision points of an algorithm.
Heuristic	A shortcut strategy used to solve problems more quickly, though not always perfectly.
Input	The information or data given to an algorithm at the start.
Instruction	A specific command or direction that tells a system what to do.
Iteration / Loop	Repeating steps in an algorithm until a condition is met.
Optimization	Improving an algorithm to make it faster, more accurate, or more efficient.
Output	The result or action produced after an algorithm runs.
Problem-Solving	The process of identifying a problem and creating steps to reach a solution.
Process	A series of actions or steps taken to achieve a goal.
Redundancy	Unnecessary repetition of steps in an algorithm.
Searching Algorithm	An algorithm used to find specific information within a dataset.
Sequence	The specific order in which steps are followed to achieve the correct result.
Sorting Algorithm	An algorithm that arranges data into a specific order (such as alphabetical or numerical).
Precision	Being exact and clear in instructions to ensure correct results.

Module Lessons

Lesson 1 | Understanding Algorithms

Lesson 2 | Designing Algorithms to Solve Problems

Lesson 3 | Analyzing Algorithms

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