The document discusses key concepts in data structures and algorithms including pseudocode, abstract data types (ADTs), data structures, and algorithm efficiency. It covers using pseudocode to represent algorithms, defining ADTs as a data declaration coupled with meaningful operations, implementing common data structures like arrays and linked lists, and analyzing algorithm efficiency using Big-O notation.
1. Linear Algebra for Machine Learning: Linear SystemsCeni Babaoglu, PhD
The seminar series will focus on the mathematical background needed for machine learning. The first set of the seminars will be on "Linear Algebra for Machine Learning". Here are the slides of the first part which is giving a short overview of matrices and discussing linear systems.
This chapter shows how to use knowledge about the wlorld to make decisions even when the
outcomes of an action are uncertain and the rewards for acting might not be reaped until many
actions have passed. The main points are as follows:
e Sequential decision problems in uncertain envirsinments,also called Markov decision
processes, or MDPs, are defined by a transition model specifying the probabilistic
outcomes of actions and a reward function specifying the reward in each state.
o The utility of a state sequence is the sum of all the rewards over the sequence, possibly
discounted over time. The solution of an MDP is a policy that associates a decision
with every state that the agent might reach. An optimal policy maximizes the utility of
the state sequences encountered when it is execut~ed.
e The utility of a state is the expected utility of the state sequences encountered when
an optimal policy is executed, starting in that state. The value iteration algorithm for
solving MDPs works by iteratively solving the equations relating the utilities of each
state to that of its neighbors.
Policy iteration alternates between calculating the utilities of states under the current
policy and improving the current policy with respect to the current utilities.
* Partially observable MDPs, or POMDPs, are much more difficult to solve than are
MDPs. They can be solved by conversion to an MDP in the continuous space of belief
states. Optimal behavior in POMDPs includes information gathering to reduce uncertainty and therefore make better decisions in the fiuture.
A decision-theoretic agent can be constructed for POMDP environments. The agent
uses a dynamic decision network to represent the transition and observation models,
to update its belief state, and to project forward possible action sequences.
Game theory describes rational behavior for agents in situations where multiple agents
interact simultaneously. Solutions of games are Nash equilibria-strategy profiles in
which no agent has an incentive to deviate from the specified strategy.
Mechanism design can be used to set the rules by which agents will interact, in order
to maximize some global utility through the operation of individually rational agents.
Sometimes, mechanisms exist that achieve this goal without requiring each agent to
consider the choices made by other agents.
We shall return to the world of MDPs and POMDP in Chapter 21, when we study reinforcement learning methods that allow an agent to improve its behavior from experience in sequential, uncertain environments.
1. Linear Algebra for Machine Learning: Linear SystemsCeni Babaoglu, PhD
The seminar series will focus on the mathematical background needed for machine learning. The first set of the seminars will be on "Linear Algebra for Machine Learning". Here are the slides of the first part which is giving a short overview of matrices and discussing linear systems.
This chapter shows how to use knowledge about the wlorld to make decisions even when the
outcomes of an action are uncertain and the rewards for acting might not be reaped until many
actions have passed. The main points are as follows:
e Sequential decision problems in uncertain envirsinments,also called Markov decision
processes, or MDPs, are defined by a transition model specifying the probabilistic
outcomes of actions and a reward function specifying the reward in each state.
o The utility of a state sequence is the sum of all the rewards over the sequence, possibly
discounted over time. The solution of an MDP is a policy that associates a decision
with every state that the agent might reach. An optimal policy maximizes the utility of
the state sequences encountered when it is execut~ed.
e The utility of a state is the expected utility of the state sequences encountered when
an optimal policy is executed, starting in that state. The value iteration algorithm for
solving MDPs works by iteratively solving the equations relating the utilities of each
state to that of its neighbors.
Policy iteration alternates between calculating the utilities of states under the current
policy and improving the current policy with respect to the current utilities.
* Partially observable MDPs, or POMDPs, are much more difficult to solve than are
MDPs. They can be solved by conversion to an MDP in the continuous space of belief
states. Optimal behavior in POMDPs includes information gathering to reduce uncertainty and therefore make better decisions in the fiuture.
A decision-theoretic agent can be constructed for POMDP environments. The agent
uses a dynamic decision network to represent the transition and observation models,
to update its belief state, and to project forward possible action sequences.
Game theory describes rational behavior for agents in situations where multiple agents
interact simultaneously. Solutions of games are Nash equilibria-strategy profiles in
which no agent has an incentive to deviate from the specified strategy.
Mechanism design can be used to set the rules by which agents will interact, in order
to maximize some global utility through the operation of individually rational agents.
Sometimes, mechanisms exist that achieve this goal without requiring each agent to
consider the choices made by other agents.
We shall return to the world of MDPs and POMDP in Chapter 21, when we study reinforcement learning methods that allow an agent to improve its behavior from experience in sequential, uncertain environments.
A cellular automaton is a discrete model studied in computer science, mathematics, physics, complexity science, theoretical biology and microstructure modeling.
This presentation is a basic introduction.
A cellular automaton is a discrete model studied in computer science, mathematics, physics, complexity science, theoretical biology and microstructure modeling.
This presentation is a basic introduction.
1. Algorithm and characteristics of an algorithm.
2. Rules to be followed for design and analysis of an algorithm.
3. The differentiation of data structures, file structures, and storage structures.
4. Top-down and bottom-up design approaches through examples.
5. Rules to be followed while writing the pseudo code of an algorithm.
6. Abstract data type and its necessity in a program.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
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.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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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.
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Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
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.
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.
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.
Biological screening of herbal drugs: Introduction and Need for
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for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
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Basic concepts
1. Data Structures: A Pseudocode Approach with C, Second Edition1
Chapter 1Chapter 1
Objectives
• Use pseudocode in the development of algorithms
• Understand the need for Abstract Data Type (ADT)
• Understand the implementation of ADTs
• Use void pointers and pointer to functions
• Understand the role of Big-O notation
Basic ConceptsBasic Concepts
2. Data Structures: A Pseudocode Approach with C, Second Edition2
Pseudocode
Pseudocode is an English-like representation of the algorithm logic. ItPseudocode is an English-like representation of the algorithm logic. It
consists of an extended version of the basic algorithmic constructs:consists of an extended version of the basic algorithmic constructs:
sequence, selection, and iterationsequence, selection, and iteration..
•Algorithm Header
•Purpose, Condition, and Return
•Statement Numbers
•Variables
•Statment Constructs
•Algorithm Analysis
5. Data Structures: A Pseudocode Approach with C, Second Edition5
The Abstract Data Type
An ADT consists of a data declaration packagedAn ADT consists of a data declaration packaged
together with the operations that are meaningfultogether with the operations that are meaningful
on the data while embodying the structuredon the data while embodying the structured
principles of encapsulation and data hiding. In this sectionprinciples of encapsulation and data hiding. In this section
we define the basic parts of an ADT.we define the basic parts of an ADT.
•Atomic and Composite Data
•Data Type
•Data Structure
•Abstract Data Type
7. Data Structures: A Pseudocode Approach with C, Second Edition7
Data Structure
Aggregation of atomic and composite data into a set with defined
relationships. Structure refers to a set of rules that hold the data
together.
• A combination of elements in which each is either a data type or another
data structure.
• A set of associations of relationship involving combined elements.
Example:
9. Data Structures: A Pseudocode Approach with C, Second Edition9
Abstract Data Type
ADT users are NOT concerned with how the task is done but rather what it
can do.
An abstract data type is a data declaration packaged together with the
operations that are meaningful for the data type.
We encapsulate the data and the operations on the data, and then hide
them from the user.
All references to and manipulation of the data in a data structure are handled
through defined interfaces to the structure.
10. Data Structures: A Pseudocode Approach with C, Second Edition10
Model for an Abstract Data Type
In this section we provide a conceptual
model for an Abstract Data Type (ADT).
• ADT Operation – passage like
• ADT Data Structure – controlled
entirely
12. Data Structures: A Pseudocode Approach with C, Second Edition12
ADT Implementations
There are two basic structures we can use to
implement an ADT list: arrays and linked lists.
In this section we discuss the basic
linked-list implementation.
• Array Implementation
• Linked List Implemenation
16. Data Structures: A Pseudocode Approach with C, Second Edition16
Generic Code for ADT
In this section we discuss and provide examples
of two C tools that are required to implement
an ADT.
• Pointer to Void
• Pointer to Function
35. Data Structures: A Pseudocode Approach with C, Second Edition35
Algorithm Efficiency
To design and implement algorithms, programmersTo design and implement algorithms, programmers
must have a basic understanding of what constitutesmust have a basic understanding of what constitutes
good, efficient algorithms.good, efficient algorithms.
Linear Loops
-Efficiency is a function of the number of intstructions.
- Loop update either adds or subtracts.
• Logarithmic Loops
-The controlling variable is either multiplied or divided in each iteration.
- The number of iteration is a function of the multiplier or divisor.
• Nested Loops
- The number of iterations is the total number which is the product of the number of
iterations in the inner loop and number of iterations in the outer loop.
• Big-O Notation
-Not concerned with exact measurement of efficiency but with the magnitude.
- A dominant factor determines the magnitute.