UGC NET 2021 | Theory Questions Artificial Intelligence | Computer Science | Puneet Mam | Gradeup

Introduction

Introduction
Good morning, everyone! A warm welcome to all of you. Today's session will delve into the world of Artificial Intelligence (AI) as it pertains to your UGC NET exam preparations. This article will cover various questions related to AI theory, designed to help you grasp the concepts needed for the exam.

Class Schedule and Participation
As a reminder, classes are held every Tuesday, Wednesday, Thursday, Friday, and Saturday. You can download our app to access these classes and join our educational community. Don't forget to click the “prepare” icon for additional resources and class updates! Special mini-series sessions will be running this Thursday, Friday, and Saturday at 11 am, where we will tackle similar questions to what will appear in the NET examination.

Our aim during these sessions is to provide you with clarity on conceptual topics such as informed and uninformed search techniques, migration processes, and more. You will also have a chance to participate in question-and-answer sessions and practice similar questions taken from recent exams.

Understanding Informed vs. Uninformed Search Techniques
To dive into our first discussion point, we need to understand the difference between informed and uninformed search techniques. We had previously covered hill climbing methods and their relation to informed search techniques. It’s essential to know that an informed search utilizes additional knowledge, while uninformed searches do not leverage any external data.

Important Examination Concepts
We discussed key topics such as the migration process in logic, the role of quantifiers, and various learning techniques within AI. For instance, supervised learning requires labeled input data, whereas unsupervised learning does not. Reinforcement learning is another crucial concept that allows agents to learn through interaction with their environment, making decisions that yield the most reward over time.

As we engage with the material, please recall that the tests will feature questions on various AI agents: simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents. We will also explain the fundamentals of expert systems, including their interface and inference mechanisms.

Conclusion and Call to Action
In wrapping up our session, remember to participate actively. You should practice daily and join the Tuesday and Wednesday classes to bolster your understanding of the content. Your engagement is critical for your success in the NET exam.

Let’s keep the momentum going as we continue to explore AI topics. Don’t forget to press the like button and subscribe for further updates and notifications.

Keyword

• Artificial Intelligence
• UGC NET
• Informed Search
• Uninformed Search
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Expert Systems
• Learning Techniques

FAQ

Q1: What is the difference between informed and uninformed search techniques?
A1: Informed search techniques utilize additional knowledge to find the goal more efficiently, while uninformed search techniques do not leverage any external data.

Q2: What is supervised learning?
A2: Supervised learning is a type of machine learning where an algorithm is trained on labeled input data, allowing it to make predictions or decisions based on that data.

Q3: What are expert systems?
A3: Expert systems are computer programs that emulate the decision-making ability of a human expert. They rely on a knowledge base and inference rules to solve problems in specific domains.

Q4: What is reinforcement learning?
A4: Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward.

Q5: When are the classes held?
A5: Classes are conducted from Tuesday to Saturday, and are accessible through the learning app.