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Introduction to Artificial Intelligence - CS 230

Introduces AI concepts, search, classification, machine learning basics, neural networks, applications, limitations, and responsible AI.

Study Tools

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Study guides

7

5 Neural Networks

Learn how neural networks transform inputs, use activation functions to represent patterns, and train their parameters to improve predictions.
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7 AI Limitations and Responsible Use

Learn what AI systems can and cannot do, how their risks interact, and how to evaluate and manage those risks in context.
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3 Classification Fundamentals

Learn how classification uses labeled examples, how to evaluate performance on unseen data, and how to interpret common binary-classification metrics.
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6 AI Applications

Learn how AI applications turn data into useful outputs across everyday tools and essential fields, and why evaluation and human review matter.
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2 Search and Problem Solving

Learn how to formulate search problems and choose uninformed or heuristic-guided algorithms based on costs, goals, memory, and solution guarantees.
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4 Machine Learning Basics

Learn how machine learning projects move from task definition and data preparation through model evaluation and monitoring, and how to recognize common sources of error.
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1 Foundations of Artificial Intelligence

Learn what artificial intelligence encompasses, how its common components work, how the field developed, and how learning-based systems differ from conventional rule-based software.
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Quizzes

7

7 AI Limitations and Responsible Use

Apply principles for understanding AI limitations, identifying risks, and using AI responsibly.
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5 Neural Networks

A quiz on neural network structure, activation functions, and training.
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2 Search and Problem Solving

A medium-difficulty quiz on formulating search problems and applying search strategies, graph representations, and heuristics.
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1 Foundations of Artificial Intelligence

A medium-difficulty quiz on foundational AI concepts, approaches, terminology, and history.
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4 Machine Learning Basics

Fourteen medium-difficulty questions on machine learning workflows, learning types, model evaluation, and common sources of error.
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6 AI Applications

A medium-difficulty quiz on how AI applications use data, produce outputs, and support decisions across everyday tools, health care, transportation, agriculture, science, and energy.
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3 Classification Fundamentals

A medium-difficulty quiz on classification fundamentals, including how classifiers use features and labels, how to evaluate predictions, and how to choose suitable data-splitting practices.
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Flashcards

7

7 AI Limitations and Responsible Use

Review AI capabilities, limitations, key risks, and the practices that support responsible AI design and use.
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3 Classification Fundamentals

Review classification fundamentals, including labeled data, train-test separation, confusion-matrix outcomes, and common evaluation metrics.
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2 Search and Problem Solving

Learn how search problems are represented and how uninformed and heuristic-guided algorithms explore possible solutions.
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5 Neural Networks

Review how neural networks process inputs, use activation functions, and learn through training and deep learning.
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6 AI Applications

Explore how AI applications turn data into useful outputs across everyday tools, health care, transportation, agriculture, science, energy, and buildings.
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1 Foundations of Artificial Intelligence

Review foundational AI concepts, key terminology, the field’s early history, and how learning-based systems differ from rule-based software.
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4 Machine Learning Basics

Review core machine learning workflows, learning types, model evaluation, and common causes of poor performance.
Flashcards · Updated