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Machine Learning Fundamentals - CS 240

Covers datasets, features, training and testing, regression, classification, clustering, model evaluation, and overfitting.

Study Tools

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Study guides

8

8 Overfitting and Generalization

Learn how overfitting and underfitting affect performance on unseen data, how to diagnose them, and how to improve generalization through suitable model choices and careful evaluation.
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7 Model Evaluation

Learn how to evaluate regression and classification models, interpret common metrics, and choose measures and thresholds that reflect real-world error costs.
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4 Regression

Learn how regression predicts numeric targets, how linear models are fitted and interpreted, and how to evaluate their performance on unseen data.
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6 Clustering

Learn how clustering groups unlabeled examples, how common clustering methods differ, and how to assess whether their results are useful.
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5 Classification

Learn how classification assigns examples to categories, how classifiers separate predictions, and how common models and thresholds produce labels.
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3 Training and Testing

Learn how to divide data for model development, fit preprocessing safely, and design evaluations that avoid leakage.
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2 Datasets and Features

Learn how machine-learning datasets are structured, what makes their data suitable for a prediction task, and how to prepare them without compromising evaluation.
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1 Introduction to Machine Learning

Learn how machine-learning models use data, how common tasks and workflows differ, and how to evaluate whether a model performs well on new examples.
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Quizzes

8

8 Overfitting and Generalization

Assess understanding of overfitting, underfitting, model evaluation, and practices that support generalization.
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7 Model Evaluation

A medium-difficulty quiz on choosing and interpreting model evaluation measures, understanding classification errors, and selecting evaluation practices suited to an application.
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6 Clustering

A medium-difficulty quiz on clustering concepts, methods, applications, and interpretation.
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3 Training and Testing

A medium-difficulty quiz on how to use training, validation, and test data, fit preprocessing safely, and design splits that support reliable evaluation.
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5 Classification

Practice identifying classification tasks, interpreting model behavior, and applying classification concepts.
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1 Introduction to Machine Learning

A medium-difficulty quiz applying core concepts and workflows from the introduction to machine learning.
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2 Datasets and Features

A medium-difficulty quiz on identifying dataset components, assessing data quality, and preparing data for machine learning.
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4 Regression

A medium-difficulty quiz on regression models, interpreting predictions and coefficients, and assessing model performance.
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Flashcards

8

7 Model Evaluation

Learn how to evaluate regression and classification models, interpret common metrics, and choose measures that reflect error costs.
Flashcards · Updated

8 Overfitting and Generalization

Review how to recognize overfitting and underfitting, improve generalization, and evaluate models without data leakage.
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3 Training and Testing

Learn how to divide data, fit models and preprocessing safely, and prevent leakage when evaluating machine-learning models.
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1 Introduction to Machine Learning

Learn the core concepts, problem types, workflow, and evaluation principles of introductory machine learning.
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2 Datasets and Features

Review how datasets, samples, features, and labels support machine learning, and how to prepare data reliably for modeling and evaluation.
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6 Clustering

Learn how clustering groups unlabeled examples, how common clustering methods differ, and how to interpret their results.
Flashcards · Updated

4 Regression

Review how regression predicts numeric targets, how linear models are fitted, and how their predictions are evaluated.
Flashcards · Updated

5 Classification

Review how classification assigns categories to examples, how decision boundaries work, and how common classifiers produce predictions.
Flashcards · Updated