lecture 1 Flashcards
What is machine learning?
A field of AI that enables computers to learn patterns from data without being explicitly programmed.
What are the three main types of machine learning?
Supervised learning, unsupervised learning, and reinforcement learning.
What is supervised learning?
A type of learning where the model is trained on labeled data.
What is unsupervised learning?
A type of learning where the model finds patterns in unlabeled data.
What is reinforcement learning?
A type of learning where an agent interacts with an environment and receives rewards.
What is an example of supervised learning?
Predicting house prices based on features like size and location.
What is an example of unsupervised learning?
Grouping customers based on purchasing behavior (clustering).
What is an example of reinforcement learning?
Training an AI to play chess by rewarding good moves.
What is a training dataset?
A dataset used to teach a machine learning model.
What is a test dataset?
A dataset used to evaluate the performance of a trained model.
What is the difference between regression and classification?
Regression predicts continuous values, while classification predicts categories.
What is an example of a classification problem?
Determining whether an email is spam or not.
What is an example of a regression problem?
Predicting the temperature for tomorrow.
What is feature engineering?
The process of selecting and transforming input variables to improve model performance.
What is overfitting?
When a model learns noise in the training data instead of generalizing to new data.
What is underfitting?
When a model is too simple and fails to capture important patterns in the data.
What is a loss function?
A function that measures how well a model’s predictions match the actual values.
What is optimization in machine learning?
The process of adjusting model parameters to minimize the loss function.
What is gradient descent?
An algorithm used to find the optimal parameters by minimizing the loss function.
What is the difference between batch and stochastic gradient descent?
Batch uses the entire dataset for updates, while stochastic updates after each data point.
What is cross-validation?
A technique to assess model performance by splitting the data into multiple subsets.
What is bias in machine learning?
A model’s tendency to make systematic errors.
What is variance in machine learning?
How much a model’s predictions change based on different training sets.
What is the bias-variance tradeoff?
The balance between underfitting (high bias) and overfitting (high variance).
What is a confusion matrix?
A table used to evaluate classification model performance.
What are precision and recall?
Precision measures accuracy among positive predictions; recall measures how many positives were identified correctly.
What is F1-score?
The harmonic mean of precision and recall, balancing both metrics.
What is the purpose of model evaluation?
To determine how well a trained model performs on unseen data.
