Module 2

artificial intelligence

refers to the simulation of human intelligence in machines that are programmed to think and learn

dartmouth conference

• The term "Artificial Intelligence" was coined in 1956 at the ________.

machine learning

Core Concepts and Technologies:

Algorithms that allow computers to learn from data without being explicitly programmed.

deep learning

Core Concepts and Technologies:

A subset of ML using neural networks with many layers to model complex patterns.

natural language processing

Core Concepts and Technologies:

Enables machines to understand and generate human language.

computer vision

Core Concepts and Technologies:

Allows machines to interpret and make decisions based on visual data.

large language model

Core Concepts and Technologies:

AI systems trained on vast textual data to generate human-like responses

healthcare

Applications of AI:

AI is used for diagnostics, personalized medicine, and robotic surgery.

education

Applications of AI:

AI tools like NotebookLM and Zotero assist in organizing research and generating summaries

business

Applications of AI:

AI powers recommendation systems, customer service bots, and fraud detection.

creative industries

Applications of AI:

Generative AI creates art, music, and literature

scientific research

Applications of AI:

AI accelerates data analysis and hypothesis generation

bias and fairness

Ethical and Societal Implications:

AI systems can inherit biases from training data, leading to unfair outcomes.

privacy

Ethical and Societal Implications:

AI's ability to analyze personal data raises concerns about surveillance and data protection.

job displacement

Ethical and Societal Implications:

Automation may replace certain jobs, requiring workforce reskilling.

academic integrity

Ethical and Societal Implications:

Use of generative AI in writing must be guided by ethical standards and institutional policies

explainable ai

Making AI decisions transparent and understandable.

ai governance

Developing policies and frameworks to regulate AI development and use.

human-ai collaboration

Enhancing productivity through synergistic interaction between humans and AI.

machine learning

is a subset of artificial intelligence (AI) that focuses on building systems that learn from data and improve their performance over
time without being explicitly programmed.

supervised learning

TYPES OF MACHINE LEARNING PARADIGMS:

________

unsupervised learning

semi-supervised learning

reinforcement learning

neural networks

deep learning

unsupervised learning

TYPES OF MACHINE LEARNING PARADIGMS:

supervised learning

________

semi-supervised learning

reinforcement learning

neural networks

deep learning

semi-supervised learning

TYPES OF MACHINE LEARNING PARADIGMS:

supervised learning

unsupervised learning

________

reinforcement learning

neural networks

deep learning

reinforcement learning

TYPES OF MACHINE LEARNING PARADIGMS:

supervised learning

unsupervised learning

semi-supervised learning

________

neural networks

deep learning

neural networks

TYPES OF MACHINE LEARNING PARADIGMS:

unsupervised learning

semi-supervised learning

reinforcement learning

________

deep learning

deep learning

TYPES OF MACHINE LEARNING PARADIGMS:

supervised learning

unsupervised learning

semi-supervised learning

reinforcement learning

neural networks

________

supervised learning

is a type of machine learning where the

model is trained on labeled data. The model learns to map

input data to the correct output based on the provided labels

supervised learning

The model is trained using a dataset that includes both input

data and corresponding output labels. During training, the

model makes predictions and adjusts its parameters to minimize

the difference between its predictions and the actual labels.

unsupervised learning

is a type of machine learning where the

model is trained on unlabeled data. The model tries to find

patterns and relationships in the data without any guidance

from labels.

unsupervised learning

The model analyzes the input data and identifies patterns or

clusters based on similarities between data points. It does not

have any predefined labels to guide its learning process

semi-supervised learning

It bridges the gap between supervised learning (which relies solely on labeled data) and unsupervised

learning (which learns from unlabeled data without explicit guidance)

reinforcement learning

is a type of machine learning where the model

learns to make decisions by interacting with an environment. The model

receives rewards or penalties based on its actions and adjusts its

strategy to maximize cumulative rewards

reinforcement learning

The model takes actions in an environment and receives feedback in

the form of rewards or penalties. It uses this feedback to update its

policy and improve its decision-making over time

neural network

are a type of machine learning model inspired by the

structure and function of the human brain. They consist of layers of

interconnected nodes (neurons) that process and transform input data

neural network

are composed of an input layer, hidden layers, and an

output layer.

deep learning

is a subset of machine learning that involves training

neural networks with many layers (deep neural networks). It is

particularly effective for tasks involving large amounts of data and

complex patterns.