This document discusses two inductive-analytical approaches to learning from data: 1) minimizing errors between a hypothesis and training examples as well as errors between the hypothesis and domain theory, with weights determining the importance of each, and 2) using Bayes' theorem to calculate the posterior probability of a hypothesis given the data and prior knowledge. It also describes three ways prior knowledge can alter a hypothesis space search: using prior knowledge to derive the initial hypothesis, alter the search objective to fit the data and theory, and alter the available search steps.
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...Ashish Duggal
The following are the topics in this presentation Prepositional Logic (PL) and First-order Predicate Logic (FOPL) is used for knowledge representation in artificial intelligence (AI).
There are also sub-topics in this presentation like logical connective, atomic sentence, complex sentence, and quantifiers.
This PPT is very helpful for Computer science and Computer Engineer
(B.C.A., M.C.A., B.TECH. , M.TECH.)
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
NLP techniques used for Spell checking to recommend find error in the written word and also suggest a relevant word.
Algorithm: Jaccard Coefficient, The Levenshtein Distance
Artificial Intelligence (AI) | Prepositional logic (PL)and first order predic...Ashish Duggal
The following are the topics in this presentation Prepositional Logic (PL) and First-order Predicate Logic (FOPL) is used for knowledge representation in artificial intelligence (AI).
There are also sub-topics in this presentation like logical connective, atomic sentence, complex sentence, and quantifiers.
This PPT is very helpful for Computer science and Computer Engineer
(B.C.A., M.C.A., B.TECH. , M.TECH.)
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
NLP techniques used for Spell checking to recommend find error in the written word and also suggest a relevant word.
Algorithm: Jaccard Coefficient, The Levenshtein Distance
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2. 2.1 The Learning Problem
• Given:
• A set of training examples D, possibly
containing errors.
• A domain theory B, possibly containing errors.
• A space of candidate hypotheses H.
• Determine:
• A hypothesis that best fits the training
examples and domain theory.
3. • There are 2 approaches:
• 1. To find best fit find errorD(h),errorB(h)
errorD(h) is defined to be the proportion of
examples from D that are misclassified by h.
errorB(h) of h with respect to a domain theory
B to be the probability that h will disagree with
B on the classification of a randomly drawn
instance.
we could require the hypothesis that minimizes
some combined measure of these errors, such
as
4. • It is not clear what values to assign to kB and kD
to specify the relative importance of fitting the
data versus fitting the theory. If we have a very
poor theory and a great deal of reliable data, it
will be best to weight errorD(h) more heavily.
• Given a strong theory and a small sample of very
noisy data, the best results would be obtained by
weighting errorB(h) more heavily. Of course if the
learner does not know in advance the quality of
the domain theory or training data, it will be
unclear how it should weight these two error
components.
5. • 2.Bayes theorem perspective.
• Bayes theorem computes this posterior
probability based on the observed data D,
together with prior knowledge in the form of
P(h), P(D), and P(Dlh).
• The Bayesian view is that one should simply
choose the hypothesis whose posterior
probability is greatest, and that Bayes
theorem provides the proper method for
weighting the contribution of this prior
knowledge and observed data.
6. • Unfortunately, Bayes theorem implicitly assumes
pefect knowledge about the probability distributions
P(h), P(D), and P(Dlh). When these quantities are
only imperfectly known, Bayes theorem alone does
not prescribe how to combine them with the
observed data.
• (One possible approach in such cases is to assume
prior probability distributions over P(h), P(D), and
P(D1h) themselves, then calculate the expected
value of the posterior P (h / D) .
• we will simply say that the learning problem is to
minimize some combined measure of the error of
the hypothesis over the data and the domain theory.
7. 2.2 Hypothesis Space Search
• We can characterize most learning methods as
search algorithms by describing the hypothesis
space H they search, the initial hypothesis ho at
which they begin their search, the set of search
operators O that define individual search steps,
and the goal criterion G that specifies the
search objective.
8. Three different methods for using prior
knowledge to alter the search performed
by purely inductive methods.
• Use prior knowledge to derive an initial hypothesis from
which to begin the search.:
• In this approach the domain theory B is used to construct
an initial hypothesis ho that is consistent with B. A
standard inductive method is then applied, starting with
the i ho.
• For example, the KBANN system learns artificial neural
networks in It uses prior knowledge to design the
interconnections and weights for an initial network, so that
this initial network is perfectly consistent with the given
domain theory. This initial network hypothesis is then
refined inductively using the BACKPROPAGATaIlOgNor ithm
and available data. Beginning the search at a hypothesis
consistent with the domain theory makes it more likely that
the final output hypothesis will better fit this theory.
9. • Use prior knowledge to alter the objective of the
hypothesis space search:
• The goal criterion G is modified to require that
the output hypothesis fits the domain theory as
well as the training examples.
• For example, the EBNN system learns neural
networks ,Whereas inductive learning of neural
networks performs gradient descent search to
minimize the squared error of the network over
the training data, EBNN performs gradient
descent to optimize a different criterion.
• This modified criterion includes an additional
term that measures the error of the learned
network relative to the domain theory.
10. • Use prior knowledge to alter the available search
steps.
• In this approach, the set of search operators 0 is altered by
the domain theory.
• For example, the FOCL system learns sets of Horn clauses.
It is based on the inductive system FOIL, which conducts a
greedy search through the space of possible Horn clauses, at
each step revising its current hypothesis by adding a single
new literal.
• FOCL uses the domain theory to expand the set of
alternatives available when revising the hypothesis, allowing
the addition of multiple literals in a single search step when
warranted by the domain theory.
• FOCL allows single-step moves through the hypothesis space
that would correspond to many steps using the original
inductive algorithm. These "macro-moves" can dramatically
alter the course of the search, so that the final hypothesis
found consistent with the data is different from the one that
would be found using only the inductive search steps.