ITCT101 Module 2: Understanding AI & ML

<p>Self-driving vehicles for transportation. (C)</p> Signup and view all the answers

<p>Reinforcement Learning (A)</p> Signup and view all the answers

<p>To automate tasks and improve efficiency. (C)</p> Signup and view all the answers

<p>Supervised learning uses labeled data, while unsupervised learning uses unlabeled data. (D)</p> Signup and view all the answers

<p>John McCarthy and researchers, 1956 (C)</p> Signup and view all the answers

<p>It's a major milestone in AI's ability to defeat humans in complex games. (D)</p> Signup and view all the answers

<p>Grouping customers into different segments based on their purchasing behavior. (B)</p> Signup and view all the answers

<p>To understand how AI is impacting everyday life. (A)</p> Signup and view all the answers

<p>To reduce the number of features in the data. (D)</p> Signup and view all the answers

<p>Expertise in a specific task or domain. (B)</p> Signup and view all the answers

<p>Data cleaning (D)</p> Signup and view all the answers

<p>Emotional intelligence. (A)</p> Signup and view all the answers

<p>To provide examples for the model to learn from. (D)</p> Signup and view all the answers

<p>To evaluate the accuracy of the model. (C)</p> Signup and view all the answers

<p>A computer program that can learn from experience and improve its performance. (D)</p> Signup and view all the answers

<p>Deep Learning focuses on using Artificial Neural Networks, while Machine Learning is a broader concept. (D)</p> Signup and view all the answers

<p>Input and label (B)</p> Signup and view all the answers

<p>Training uses labeled data to create a model, while inferencing uses the model to make predictions on new data. (D)</p> Signup and view all the answers

<p>A method for adjusting the weights of Artificial Neural Networks during training (D)</p> Signup and view all the answers

<p>Increased transparency in model decision-making (D)</p> Signup and view all the answers

<p>The specific architecture of Artificial Neural Networks, particularly the many layers (B)</p> Signup and view all the answers

<p>To enable machines to learn from data and perform tasks without explicit programming. (B)</p> Signup and view all the answers

<p>1990s (B)</p> Signup and view all the answers

<p>To represent the learned relationship between input and label. (A)</p> Signup and view all the answers

<p>AI is a broader concept, while ML is a subset of AI focusing on learning from data. (A)</p> Signup and view all the answers

<p>Artificial Intelligence (C)</p> Signup and view all the answers

<p>A computer program that can play chess at a grandmaster level. (B)</p> Signup and view all the answers

<p>AGI is a theoretical concept that is not yet achieved. (A)</p> Signup and view all the answers

<p>To enable the machine to learn patterns and make predictions based on data. (D)</p> Signup and view all the answers

<p>Risk minimization: Support vector machines (1995) (A)</p> Signup and view all the answers

<p>Machine learning systems can easily understand and respond to human emotions. (A)</p> Signup and view all the answers

<p>A self-driving car that uses sensors and data to navigate. (B)</p> Signup and view all the answers

<p>Machine Learning is a subset of Artificial Intelligence. (A)</p> Signup and view all the answers

AI that performs well on specific tasks, not general intelligence.

AI that can perform any intellectual task a human can do.

Machines designed to perform tasks intelligently since 1956.

Subset of AI where machines learn from data to improve performance.

A statistical method for modeling relationships, established in 1801.

Classification method introduced in 1936 for sorting data.

A model for reasoning under uncertainty, developed in 1985.

Risk minimization method for classification, introduced in 1995.

Programs or systems that utilize AI to accomplish specific tasks.

AI used to identify and filter promotional or unwanted emails.

Machines capable of performing tasks intelligently, dating back to 1956.

A dataset used to train a model, usually containing input-output pairs.

A representation that captures the relationship between input and label in machine learning.

The process where a model uses input data to forecast an output for new cases.

A subset of machine learning based on artificial neural networks, popularized in the 2010s.

An algorithm used in training deep learning models for optimizing performance, introduced in 1986.

Computational models inspired by the human brain, used in deep learning.

Deep learning models that perform better with increasing data scales.

A learning process where models are trained using labeled data to predict outcomes.

A computer program learns from experience E regarding tasks T and performance measure P if its performance improves with experience.

A metric used to evaluate how well a machine learning model performs a task, such as accuracy or percentage of correct predictions.

A type of machine learning where an agent learns by interacting with an environment, receiving rewards or penalties based on actions taken.

A separate subset of data used to evaluate the performance of a trained machine learning model.

A defined set of rules or instructions that a machine learning model follows to learn from data and make predictions.

The main categories of machine learning approaches, including supervised, unsupervised, semi-supervised, and reinforcement learning.

A technique used in unsupervised learning to reduce the number of features in data while retaining its essential characteristics.

AI that excels in a specific task or area.

AI that can understand and learn any intellectual task like a human.

The percentage of AI technology usage by experts and the general public.

A subset of AI where machines learn from data.

A type of ML using neural networks to model complex patterns.

The field that uses scientific methods to analyze data for insights.

An AI program that plays the board game Go, known for its 2016 victory.

Different levels of AI usage; more common in expert fields than general use.