Linear Regression

These flashcards cover the key concepts, definitions, and important ideas presented in the CAP 4630 Artificial Intelligence lecture notes by Dr. Chen Chen.

What are the main goals of Artificial Intelligence (AI) as defined in the lecture?

Thinking humanly, thinking rationally, acting humanly, acting rationally.

What is the Turing Test?

A test designed to determine if a machine can produce responses indistinguishable from a human.

Who developed the Turing Test?

Alan Turing.

What is the significance of the Turing Test in AI?

It serves as a benchmark for assessing a machine's ability to exhibit human-like intelligence.

What did Alan Turing predict about machines and human judges by the year 2000?

Machines would be able to fool 30% of human judges for five minutes.

How did Tom Mitchell define 'Machine Learning' in the lecture?

Machine Learning is the study of computer algorithms that improve automatically through experience.

What are the different types of learning methods in Machine Learning?

Supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, weakly-supervised learning.

What characterizes regression problems in supervised learning?

They involve predicting continuous numerical values.

What characterizes classification problems in supervised learning?

They involve predicting discrete categorical outcomes.

What is the purpose of regularization in machine learning models?

To control the complexity of the learned hypothesis and prevent overfitting.

What is bias in the context of machine learning?

Bias is the error that occurs due to overly simplistic or incorrect assumptions made by the model.

What is variance in the context of machine learning?

Variance is the error that occurs due to a model's excessive sensitivity to the specific training data, leading to fluctuations in performance on new data.

Name some applications of Machine Learning mentioned in the notes.

Computer Vision, Speech Recognition, Language Translation, Game Playing.

How do deep learning systems typically learn?

Deep learning uses neural networks to simulate human-like decision making processes.

What is the primary challenge in implementing the Turing Test for AI?

AI has traditionally struggled with 'common sense' reasoning.

What significant advancements in AI emerged in the late 2000s and 2010s?

The emergence of big data and the boom of deep learning.

How is self-supervised learning defined in the lecture?

It is a form of unsupervised learning where the data itself provides the supervisory signals with minimal human intervention.

What is the goal of feature scaling in data pre-processing?

To ensure that all features have similar scales, which allows gradient descent to converge more quickly.

What is ridge regression?

A form of regularized linear regression that adds a penalty term for large parameter values to prevent overfitting.

What is overfitting in machine learning?

Overfitting occurs when a model learns the training data too well, including its noise and specific details, resulting in poor performance on unseen data.

What is underfitting in machine learning?

Underfitting occurs when a model is too simple to capture the fundamental patterns in the training data, leading to high error rates on both training and test data.

What is the role of a loss function in machine learning?

A loss function quantifies the discrepancy between a model's predicted output and the actual target value, guiding the learning process by measuring performance.

What is Gradient Descent used for in machine learning?

Gradient Descent is an optimization algorithm used to iteratively adjust a model's parameters to minimize its loss function.

What is cross-validation?

A technique to evaluate a machine learning model's generalization ability by partitioning data into multiple training and validation sets.

Why is cross-validation important in machine learning?

It helps to obtain a more reliable estimate of a model's performance on unseen data and to detect and prevent overfitting.

What are the basic components of a Reinforcement Learning system?

An agent, an environment, states, actions, rewards, and a policy.

What is the primary concern regarding bias in AI systems?

The primary concern is that bias, often originating from biased training data, can lead to unfair or discriminatory predictions and decisions.