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Artificial Intelligence(AI)

Lec-01: Artificial Intelligence

Description

A fundamental understanding of any of the these concepts will always help you in understanding the Artificial Intelligence and helpful to move fast on in which research is prospering.

Prerequisite

Before beginning with this tutorial, you should have a fundamental understanding of Computer Concepts and Algorithm implementation and some Programming terminologies.

Topics Covered

• BFS & DFS • DLS & UCS • A Star & AO • Prepositional Logic

• First Order Logic • Forward & Backward Chaining • Scope of AI • AI Learning

For whom

This tutorial is prepared for the students at the beginner level who aspire to learn or study Artificial Intelligence.

Reference books

1.“Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig

2.“Artificial Intelligence: A New Sythesis” by Nils J Nilsson

3.“Artificial Intelligence” by Negnevitsky

4.“Artificial Intelligence: A Modern Approach” by Norvig and Russell

Q & A
Exam Questions

Q1 . What is the difference between Strong Artificial Intelligence and Weak Artificial Intelligence?
Q2 . What is an expert system? What are the characteristics of an expert system?
Q3 . What is a breadth-first search algorithm? What is a depth-first search algorithm? Explain with an example.
Q4 . What is Markov's Decision process?
Q5 . What is game theory? How is it important in AI?
Q6 . What are the different types of keys in a relational database? Give a brief introduction of partial, alternate, artificial, and compound keys?
Q7 . Explain different ways to evaluate the performance of the ML model.
Q8 . What is Bias–Variance tradeoff?
Q9 . What are the steps involved in the gradient descent algorithm?
Q10 . What are the intermediate tensors? What do you understand by the session in Tensor Flow? How to run Tensor Flow on Hadoop?

The A* algorithm is based on the best priority search method, because it gives the idea of optimization and fast path selection, all features are in the A* algorithm.

Artificial intelligence neural networks can mathematically model the workings of biological brains, enabling machines to think and learn like humans-enabling them to recognize speech, objects, and animals like us.

The Tower of Hanoi is a mathematical puzzle that illustrates how recursion can be used as a means to construct algorithms to solve specific problems. Using decision trees and breadth-first search (BFS) algorithms in AI, we can solve the Hanoi Tower.

AI covers lots of domains or subsets, and some main domains are given below: • Machine Learning • Deep Learning • Neural Network • Expert System • Fuzzy Logic • Natural Language Processing • Robotics • Speech Recognition.

The top-down parser first assumes a sentence, and then predicts the lower-level components in turn, until each preceding symbol is written.

The Turing test is a method of testing the ability of a machine to match human intelligence. A machine is used to challenge human intelligence, and when it passes the test, it is considered intelligent. However, without a full understanding of how to imitate humans, machines can be considered intelligent.

In AI, Prolog is a logic-based programming language. The “depth-first search” method takes less memory.

Alpha-Beta pruning is a search algorithm designed to reduce the number of nodes searched in the search tree by the minimax algorithm. It can be applied to the "n" depth and can trim entire subtrees and leaves.

First-order predicate logic is a collection of formal systems, in which each sentence is divided into a subject and a predicate. The predicate only refers to one topic and can modify or define the attributes of the topic.

Hybrid Bayesian networks contain discrete variables and continuous variables.

Anything that perceives its environment through sensors and acts on the environment through effectors is called an agent. Agents include robots, programs, and humans.

In some order plans, it is better to search for possible plans instead of searching for possible situations. The idea is to build a plan step by step.

a) Add an operator (action) b) Add an ordering constraint between operators

"Attachment" is not considered an ideal attribute of a system based on logical rules.

In artificial intelligence, a neural network is a simulation of a biological nervous system. It receives data based on algorithms and empirical data, processes the data, and gives the output.

There are mainly two types of models: parametric models and non-parametric models. Here, the parameters are the predictor variables used to build the machine learning model. The description of these models is as follows: Parametric model: The parametric model uses a fixed number of parameters to create an ML model. It considers strong assumptions about the data. Examples of parametric models include linear regression, logistic regression, naive Bayes, perceptron’s, etc. Non-parametric models: Non-parametric models use a flexible number of parameters. It considers some assumptions about the data. These models are suitable for higher data without prior knowledge. Examples of non-parametric models include decision trees, K nearest neighbors, SVM with Gaussian kernel, etc.

The heuristic function ranks the alternatives in the search algorithm in each branching step based on the available information to decide which branch to follow.

• Hyper-parameters are variables that define the structure of the network. For example, variables such as the learning rate, define how the network is trained. • They are used to define the number of hidden layers that must be present in a network. • More hidden units can increase the accuracy of the network, whereas a lesser number of units may cause under-fitting.

The versatility is the ease with which the method can be applied to different application fields.

FOPL stands for First Order Predicate Logic, Predicate Logic provides a) A language to express assertions about certain “World” b) An inference system to deductive apparatus whereby we may conclude such assertion c) A semantic-based on set theory

a) A set of constant symbols b) A set of variables c) A set of predicate symbols d) A set of function symbols e) The logical connective f) The Universal Quantifier and Existential Qualifier g) A special binary relation of equality

In an online search, it will act first and then observe the environment.

There are three types of words in the top-down inductive learning method. a) Predicates b) Equality and Inequality c) Arithmetic Literals

In propositional logic, logical reasoning algorithms can be used by a) Logical Equivalence b) Validity c) Satisfying ability

Hidden Markov model is a commonly used tool for modeling time series data or modeling sequence behavior. They are used in almost all current speech recognition systems.

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Category Computer Science
Language English
Price 69.00
Validity 1 Year
Rating 5 | 9 Reviews
Q & A 25
Exam Questions 10
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Artificial Intelligence(AI)

Lec-01: Artificial Intelligence

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Computer Science

1.Introduction & Agents

2.Uninformed Search Algorithms

3.Informed Search Algorithms

4.Propositional logic

5.First Order Logic (FOL)

6. Forward & Backward Chaining

7. Scope of AI

8. AI Learning

Artificial Intelligence(AI)

Lec-01: Artificial Intelligence

1.Introduction & Agents

2.Uninformed Search Algorithms

3.Informed Search Algorithms

4.Propositional logic

5.First Order Logic (FOL)

6. Forward & Backward Chaining

7. Scope of AI

8. AI Learning

Q1 . A* algorithm is based on which search method?

Q2 . What is artificial intelligence Neural Networks?

Q3 . What is the Tower of Hanoi?

Q4 . What are the different Domains/subsets of Artificial Intelligence?

Q5 . What is a top-down parser?

Q6 . What is the Turing test?

Q7 . What is Prolog in AI? Which search method takes less memory?

Q8 . Explain the Alpha-Beta pruning.

Q9 . What is FOPL?

Q10 . What does a hybrid Bayesian network contain?

Q11 . What is an agent in artificial intelligence?

Q12 . What does Partial order or planning involve?

Q13 . What are the two different kinds of steps that we can take in constructing a plan?

Q14 . Which property is considered as not a desirable property of a logical rule-based system?

Q15 . What is a Neural Network in Artificial Intelligence?

Q16 . What are parametric and non-parametric model?

Q17 . What is a heuristic function?

Q18 . What are the hyper parameters in Deep Neural Networks?

Q19 . What is “Generality” in AI?

Q20 . What is FOPL stands for and explain its role in Artificial Intelligence?

Q21 . What does the language of FOPL consists of

Q22 . For online search in ‘Artificial Intelligence’ which search agent operates by interleaving computation and action?

Q23 . In top-down inductive learning methods how many liberals are available? What are they?

Q24 . How logical inference can be solved in Propositional Logic?

Q25 . What is the Hidden Markov Model (HMMs) is used?

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