Artificial Intelligence Mathematics And Logarithms Pdf

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artificial intelligence mathematics and logarithms pdf

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In an online convex optimization problem a decision-maker makes a sequence of decisions, i.

Sign in. The purpose of this post is to explain the mathematics of some critical parts of the paper as well as to give some insights. The objective function loss function and regularization at iteration t that we need to minimize is the following:. It is easy to see that the XGBoost objective is a function of functions i. From the reference [1] we can see as an example that the best linear approximation for a function f x at point a is:.

Michael Rabbat

Explanation: Artificial Intelligence is a branch of Computer science, which aims to create intelligent machines so that machine can think intelligently in the same manner as a human does. He was not only the known as the father of AI but also invented the term Artificial Intelligence. Explanation: Blind Search is also known as uninformed search, and it does not contain any domain information such as closeness, location of the goal, etc. Hence the most appropriate situation that can be used for the blind search is Small-search Space.

Explanation: The Depth Search Algorithm or DFS requires very little memory as it only stores the stack of nodes from the root node to the current node. Explanation: If a robot is able to change its own trajectory as per the external conditions, then the robot is considered intelligent. Such type of agents come under the category of AI agents or Rational Agents. Explanation: Among the given languages, Perl is not commonly used for AI.

Explanation: In the year , mathematician and computing pioneer Alan Turing introduced a test to determine whether a machine can think like a human or not, which means it can demonstrate intelligence, known as the Turing Test. It was based on the "Imitation game" with some modifications. This technique is still a measure of various successful AI projects, with some updates. Explanation: Expert system is a part of AI and a computer program that is used to solve complex problems, and to give the decision-making ability like human.

It does this with the help of a Knowledge base, Inference engine, and User interface , and all these are the components of an Expert System. Explanation: A game tree is a directed graph whose nodes represent the positions in Game and edges represent the moves. Explanation: There are only two ways to solve the problems of state-space search. Explanation: Knowledge representation is the part of Artificial Intelligence that deals with AI agent thinking and how their thinking affects the intelligent behavior of agents.

A good knowledge representation requires the following properties:. With Sensors, it senses the surrounding, and with Actuators, it acts on it. Explanation: The simple reflex agent takes decisions only on the current condition and acts accordingly; it ignores the rest of history; hence it follows the Condition-action rule. Explanation: Utility-based agent uses an extra component of utility that provides a measure of success at a given state.

It decides that how efficient that state to achieve the goal, which specifies the happiness of the agent. Explanation: Rational agent has clear preference, goal, and acts in a way to maximize its performance. It is said that it always does the right things, which means it gives the best performance for each action.

Explanation: In problem-solving, the Heuristic describes the common sense or Judgemental part. Explanation: Pattern matching is a way to check a given sequence of tokens in order to determine the presence of a given character or data in the given sequence. It allows computers to understand the relationship between objects and events. Explanation: In Exploration problems, the agent does not contain the knowledge of state space and actions in advance. These are difficult problems and used in the real world.

Explanation: The Wumpus world is an example environment that is made of grids of squares surrounded by walls. Each square can have agents or objects. The world is used to demonstrate the worth of a knowledge-based agent and knowledge representation. In the environment, uncertainty arises as the agent can only perceive the close environment.

The Wumpus world is represented in below image:. Explanation: The Alpha-beta pruning can be applied to any depth of the tree and it can eliminate the entire subtree, if it is not affecting the final decision. Explanation: Resolution is also known as inference rule as it shows the complete inference rule when applied to any search algorithm.

Explanation: Complex sentences are built by combining the atomic sentences using connectives. Explanation: The False Positive Hypothesis means that according to results, you have that condition, but in reality, you don't have it. Such as for a medical test, if someone is found Positive for a disease, but actually he doesn't have that disease, then it comes under the False Positive hypothesis. Explanation: The Hybrid Bayesian network contains both discrete and continuous variables as the numerical inputs.

To define the hybrid network, both kinds of distributions are used at wide probability distribution. It is said to be the lifted version of Modus ponens. Explanation: Unification is the process of making two different logical expressions identical by finding a substitution.

Explanation: The unify algorithm takes two atomic sentences and return a unifier. It is used for the unification process. It is made up of four words:. Explanation: The successor function provides a description of all possible actions and their next states, which means their outcomes.

Explanation: The TSP or Travelling Salesman problem is about finding the shortest possible route to visit each city only once and returning to the origin city when the list of all cities and distances between each pair of cities is given.

Explanation: In the TSP problem of n cities, the time taken for traversing all cities without having prior knowledge of the length of the minimum tour will be O n! Explanation: The web crawler is an example of Intelligent agents, which is responsible for collecting resources from the Web, such as HTML documents, images, text files, etc.

Explanation: Problem-solving agents are the goal-based agents that use different search strategies and algorithms to solve a given problem. Explanation: There are several techniques of Knowledge representation in AI, and among them, one is Logical Representation. The logical representation can be done in two ways Predicate Logic and Propositional Logic , hence knowledge can be represented as both predicate and Propositional logic.

Answer: a. The sentences of Propositional logic can have answers other than True or False. Explanation: Propositional Knowledge or PL is the simplest form of logic that is used to represent the knowledge, where all the sentences are propositions. In this, each sentence is a declarative sentence that can only be either true or False. Explanation: The first-order logic is also known as the First-order predicate logic, which is another way of knowledge representation.

The FOL statements contain two parts that are s ubject and Predicate. For e. Explanation: The knowledge-based agents have the capability of making decisions and reasoning to act efficiently.

It can be viewed at three different levels, which are:. Explanation: The AI agent is the rational agent that runs in the cycle of Perceive, think, and act. Explanation: The probabilistic reasoning is used to represent uncertain knowledge, where we are not sure about the predicates.

It depends Upon Estimation, Observation, and likelihood of objects. Explanation: The inference engine is the component of the intelligent system in artificial intelligence, which applies logical rules to the knowledge base to infer new information from known facts.

The first inference engine was part of the expert system. Inference engine commonly proceeds in two modes, which are:. Answer: c. Conditional Probability has no effect or relevance on independent events. Explanation: The conditional probability is said as the probability of occurring an event when another event has already occurred. And Independent events are those that are not affected by the occurrence of other events; hence conditional probability has no effect or relevance on independents events.

Explanation: The best AI agent is one that can solve the problem on its own without any human intervention. Explanation: A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies using a directed acyclic graph.

It gives a complete description of the domain. Explanation: An algorithm is only said the complete algorithm if it ends with a solution if it exists. Answer: d. The heuristic function calculates the cost of an optimal path between the pair of states. Explanation: The heuristic function is used in Informed search in AI to find the most promising path in the search. It estimates the closeness of the current state and calculates the cost of an optimal path between the pair of states.

It is represented by h n. Explanation: The learning element improves the performance of an AI agent while solving a given problem, so that it can make better decisions.

Explanation: A decision tree is the supervised machine learning technique that can be used for both Classification and Regression problems. It reaches its destination using a Sequence of Tests. JavaTpoint offers too many high quality services. Mail us on hr javatpoint. Please mail your requirement at hr javatpoint. Duration: 1 week to 2 week. Artificial Intelligence. Deductive reasoning. Artificial Intelligence MCQ. Answer: b.

Making a machine Intelligent. Small Search Space Explanation: Blind Search is also known as uninformed search, and it does not contain any domain information such as closeness, location of the goal, etc. All of the above Explanation: All the given options are the applications of AI. Intelligent Explanation: If a robot is able to change its own trajectory as per the external conditions, then the robot is considered intelligent.

Turing Test Explanation: In the year , mathematician and computing pioneer Alan Turing introduced a test to determine whether a machine can think like a human or not, which means it can demonstrate intelligence, known as the Turing Test. All of the above Explanation: Expert system is a part of AI and a computer program that is used to solve complex problems, and to give the decision-making ability like human. Answer: C. Representational Verification Explanation: Knowledge representation is the part of Artificial Intelligence that deals with AI agent thinking and how their thinking affects the intelligent behavior of agents.

Word Vectorizing and Statistical Meaning of TF-IDF

Sign in. If you want to understand something well, try to explain it simply. XGBoost is a beautiful algorithm and the journey through it has been nothing short of illuminating. The concepts, often simple and beautiful gets lost in mathematical jargon. I had faced the same challenges while understanding the math and this is an attempt to consolidate my understanding while helping others on a similar journey. Please note that this post assumes familiarity with the boosting process in general and will just try to touch upon the intuition and math behind Gradient Boosting and XGBoost.


gle with the mathematical knowledge required to read a machine learning textbook. Having taught Negative log-likelihood. (n k.) Binomial coefficient, n.


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Business Math Book Pdf. Welcome - Grad. BSc 1st Year Mathematics Books: In the last few months, we have got hundreds of requests regarding the mathematics study material for BSc.

Sign in. The purpose of this post is to explain the mathematics of some critical parts of the paper as well as to give some insights. The objective function loss function and regularization at iteration t that we need to minimize is the following:. It is easy to see that the XGBoost objective is a function of functions i.

A statistical way of comparing two or more techniques, typically an incumbent against a new rival. The fraction of predictions that a classification model got right. In multi-class classification , accuracy is defined as follows:.

Explanation: Artificial Intelligence is a branch of Computer science, which aims to create intelligent machines so that machine can think intelligently in the same manner as a human does. He was not only the known as the father of AI but also invented the term Artificial Intelligence.

1. Introduction

About Blog Location. Download PDF. Some layers have more than one input. Mathematics of Deep Learning. Deep Learning in the Wolfram Language 1.

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XGBoost Mathematics Explained

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1 Comments

  1. Jeremy T. 11.05.2021 at 11:19

    Log interpretation is usually a significant job for asset development with relative high uncertainties and low efficiency.