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How does one build a chess-playing computer?
Issues:
1) Representation (organization)
2) Algorithm (process)
Marr’s Information Processing Levels
Computational
goal
What is the goal of
the computation,
why is it appropriate,
and what is the logic
of the strategy by
which it can be
carried out?
Representation
and algorithm
Hardware
implementation
How can this
computational goal
be implemented? In
particular, what is
the representation
for the input and
output, and what is
the algorithm for the
transformation?
How can the
representation and
algorithm be realized
physically?
Data Structures and Algorithms: Introduction
What are data structures and algorithms?
In their simplest form:
Data structures are high-level ways to think about representing data.
Algorithms are the operations we perform on some data representation.
In a more formal setting, both are components of something called an
Abstract Data Type (ADT)
(i.e., a specification that describes a data set and the operations on that data)
An ADT is independent of any particular language or implementation technique.
Formally, a data structure is an implementation of an ADT within a programming
language.
Collection: an ADT that contains a group of objects
Container: a class that implements a collection.
Data Structures and Algorithms: Introduction
Data Structures and Algorithms: Introduction
Why study data structures and algorithms?
Data structures
The way we represent things has a large influence on what we can do with
them and how easily/efficiently we can manipulate them.
Most significant problems can be viewed from the perspective of one or more
data structures.
Some example problems requiring careful representation choices include
geographic databases, chess game trees, routing problems, genetic
information, satellite imagery, etc.).
Data Structures and Algorithms: Introduction
Why study data structures and algorithms?
Algorithms
There are a variety of fundamental operations that are useful to perform on
various data structures, regardless of the specific use (e.g. adding, deleting,
finding items in lists, dictionaries, trees).
Some fundamental algorithms:
Adding an item
Deleting an item
Searching
Sorting
Testing for existence
etc.
Fundamental algorithmic topics
Recursion
Efficiency
Language
The Top 10 Algorithms
(from Computing in Science and Engineering)
Metropolis Algorithm for Monte Carlo
Simplex Method for Linear Programming
Krylov Subspace Iteration Methods
The Decomposition Approach to Matrix Computations
The Fortran Optimizing Compiler
QR Algorithm for Computing Eigenvalues
Quicksort Algorithm for Sorting
Fast Fourier Transform
Integer Relation Detection
Fast Multipole Method
http://www.computer.org/cise/articles/Top_Algorithms.htm?SMSESSION=NO
Data Structures and Algorithms: Introduction
Why study data structures and algorithms?
General
These topics are essential foundational components for further progress in
Computer Science (as are introductory programming and discrete mathematics
courses).
The whole subject matter is really the study of Abstract Data Types (ADTs).
Object-oriented languages (e.g., Java, C++) are natural for building and using
ADTs.
Java Review
Appendix A: Java Essentials
Introduction
Applications and Applets
Objects, Classes, and Methods
Static Variables
Constructors
Java Basics
Identifiers
Reserved Words
Variables
Primitive Types
Constants
Assignment Statements and Compatibilities
Type Casting
Arithmetic Operators and Expressions
Parenthesis and Precedence Rules
Increment and Decrement Operators
Named Constants
The Math Class
Public, Private, Protected
Parameter Passing
Java Review (continued)
Simple Input and Output Using the Keyboard and Screen
Screen Output
Keyboard Input using KeyboardInput class
The if-else Statement
Boolean Expressions
Nested Statements
Multiway if-else Statements
The Conditional Operator
The switch Statement
Scope
Loops
The while Statement
The for Statement
The do-while Statement
Additional loop information
Java Review (continued)
The class String
Characters within Strings
Concatenation of Strings
String Methods
The Class StringBuffer
The Class StringTokenizer
Arrays
Array Parameters and Returned Values
Initializing Arrays
Array Index Out of Bounds
Use of = and = = with Arrays
Multidimensional Arrays
Wrapper Classes
Java Review (continued)
Appendix B: Exception Handling
Basic Exception Handling
Predefined Exception Classes
Defining Your Own Exception Classes
Multiple catch Blocks
Declaring Exceptions
Exceptions That Do Not Need to Be Caught
Java Review (continued)
Appendix C: File Input and Output
Overview
Streams
The Advantage of Files
Kinds of Files
File Names
The Package java.io
Writing to a Text File Using PrintWriter
Flushing and Output File
Appending to a Text File
Reading a Text File Using BufferedReader
Testing for the End of a Text File
Reading a File Name from the Keyboard
Defining a Method to Open a Stream
I/O with Binary Files
Writing to a Binary File Using DataOutputStream
Reading from a Binary File Using DataInputStream
The Class File
Object Serialization