Algorithm basics
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An algorithm is defined as a finite set of steps that guarantees an optimal solution to a problem in a finite amount of time. An example algorithm for finding the average of two numbers in the C programming language is given, with 4 steps. The key characteristics of algorithms are that they have an input, produce an output, terminate in a finite number of steps, have definite unambiguous steps, and provide enough information at each step to produce results. Performance of algorithms is measured by time complexity and space complexity, with time complexity usually being more important. Good algorithms should run in less time and use less memory.
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Algorithmfarishah A typical programming task can be divided into two phases:
Problem-solving phase: produce an ordered sequence of steps that describe the solution of the problem this sequence of steps is called an algorithm.
Implementation phase: implement the program in some programming language.
Every algorithm must satisfy the following criteria:
Input. Zero or more quantities are externally supplied.
Output. At least one quantity is produced.
Definiteness. Each instruction must be clear and unambiguous(Unique meaning).
Finiteness. An algorithm terminates in a finite number of steps.
Effectiveness. Every instruction must be basic enough to be carried out than, means not so complex.
An algorithm is a finite set of steps defining the solution of a particular problem.
What is the difference between an algorithm and a program?
a program is an implementation of an algorithm to be run on a specific computer and operating system.
an algorithm is more abstract – it does not deal with machine-specific details – think of it as a method to solve a problem.
What is a good algorithm?
Efficient algorithms are good, we generally measure the efficiency of an algorithm based on:
Time: the algorithm should take minimum time to execute.
Space: the algorithm should use less memory.
DIFFERENCE BETWEEN ALGORITHM AND PSEUDOCODE?
An algorithm is a well-defined sequence of steps that provides a solution for a given problem, while pseudocode is one of the methods that can be used to represent an algorithm.
While algorithms can be written in natural language, pseudocode is written in a format that is closely related to high-level programming language structures.
But pseudocode does not use specific programming language syntax and therefore could be understood by programmers who are familiar with different programming languages. Additionally, transforming an algorithm presented in pseudocode to programming code could be much easier than converting an algorithm written in natural language.
But pseudocode does not use specific programming language syntax and therefore could be understood by programmers who are familiar with different programming languages.
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When this way is chooses, care should be taken, we
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Blocks are indicated with matching braces { and }.
An identifier begins with a letter. The data types of variables are not explicitly declared.
node= record
{
data type 1 data 1;
data type n data n;
node *link;
}
4. There are two Boolean values TRUE and FALSE.
Logical Operators
AND, OR, NOT
Relational Operators
<, <=,>,>=, =, !=
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a) Divide & Conquer
b) Greedy method
c) Dynamic Programming
d) Branch & Bound
e) Backtracking
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Algorithms & Complexity CalculationAkhil Kaushik The document defines algorithms and describes their characteristics and design techniques. It states that an algorithm is a step-by-step procedure to solve a problem and get the desired output. It discusses algorithm development using pseudocode and flowcharts. Various algorithm design techniques like top-down, bottom-up, incremental, divide and conquer are explained. The document also covers algorithm analysis in terms of time and space complexity and asymptotic notations like Big-O, Omega and Theta to analyze best, average and worst case running times. Common time complexities like constant, linear, quadratic, and exponential are provided with examples.
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Recurrent neural networks rnn
Recurrent neural networks rnnKuppusamy P The document discusses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It provides details on the architecture of RNNs including forward and back propagation. LSTMs are described as a type of RNN that can learn long-term dependencies using forget, input and output gates to control the cell state. Examples of applications for RNNs and LSTMs include language modeling, machine translation, speech recognition, and generating image descriptions.
Deep learning
Deep learningKuppusamy P Artificial Neural Networks
Artificial Neuron Working Principle
Activation function
Types of Activation Functions
Binary step
Linear activation function
Non-Linear Activation Functions:
ReLU
LeakyReLU
Sigmoid
Tanh
Softmax
Tuning hyper-parameters
Deep Learning
Convolutional Neural Networks (CNNs)
Pooling
Fully Connected Layer
Backward Propagation
Deep Learning Applications
Image segmentation
Image segmentationKuppusamy P Image Segmentation
Types of Image Segmentation
Semantic Segmentation
Instance Segmentation
Types of Image Segmentation Techniques based on the image properties:
Threshold Method.
Edge Based Segmentation.
Region-Based Segmentation.
Clustering Based Segmentation.
Watershed Based Method.
Artificial Neural Network Based Segmentation.
Image enhancement
Image enhancementKuppusamy P Image enhancement, Spatial domain enhancement, Point operations, Spatial operations, Frequency domain enhancement, Digital Negative, Contrast Stretching, Thresholding, Gray level slicing , Bit plane slicing, Dynamic range compression, Histogram modeling, Adaptive Histogram Equalization, Fourier Transforms, Spectral Analysis, Frequency Analysis, Color Image, Color Image Enhancement
Feature detection and matching
Feature detection and matchingKuppusamy P Image Features, Types of Features, Components of Feature Detection and Matching, Feature detection (extraction)
Feature Description , Feature Matching , Haar wavelets, Feature tracking, Invariant Local Features, Point Features / Corners
Harris operator / Harris Corner Detector, Adaptive non-maximal suppression, Feature descriptors, Edge Detection
Image processing, Noise, Noise Removal filters
Image processing, Noise, Noise Removal filtersKuppusamy P Basics of images, Digital Images, Noise, Noise Removal filters
Reference:
Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2010
Flowchart design for algorithms
Flowchart design for algorithmsKuppusamy P The document discusses flowchart design and symbols. It explains that a flowchart is a graphical representation of an algorithm that shows the sequence of steps in a process or workflow. The main advantages are that flowcharts make algorithms easier to understand visually through symbols, allow for better communication of the logic, and help split problems into smaller parts. Standard flowchart symbols are described, including shapes that represent start/stop, inputs/outputs, decisions, iterations, and connecting arrows. The key difference between an algorithm and flowchart is that an algorithm explains the steps in text while a flowchart shows the same steps graphically using standardized symbols.
Problem solving using Programming
Problem solving using ProgrammingKuppusamy P The document discusses 10 problem statements related to algorithms and flowcharts. It provides the steps to design algorithms and draw flowcharts to: 1) exchange two values with and without a temporary variable, 2) find the sum of digits in a number, 3) find the sum of integers in an array, 4) count digits in a number, 5) compute factorials, 6) compute sine functions, 7) generate Fibonacci sequences, 8) reverse digits of a number, 9) convert a decimal number to binary, and 10) references used. For each problem, it lists the steps of the algorithm and includes examples.
Parts of Computer, Hardware and Software 
Parts of Computer, Hardware and Software Kuppusamy P The document discusses the basic components and functions of computers. It defines a computer as an electronic device that accepts input data, processes it based on stored instructions, and generates output. The main components are the central processing unit (CPU), memory to store instructions and temporary data, storage devices to permanently store data, and input/output devices. It then describes the basic steps in the data processing cycle as input, processing, output, and storage. Real-world examples of smart home applications and common computer uses for personal, business, and education are provided. The four main parts of a computer system - hardware, software, data, and users - are defined at a high level.
Strings in java
Strings in javaKuppusamy P Strings in Java are objects of the String class that represent sequences of characters. Strings are immutable, meaning their contents cannot be modified once created. The StringBuffer class represents mutable strings that can be modified by methods like append(), insert(), delete(), and replace(). Some key String methods include length(), charAt(), equals(), concat(), and indexOf(), while common StringBuffer methods allow modifying the string through insertion, deletion, replacement and reversal of characters.
Java methods or Subroutines or Functions
Java methods or Subroutines or FunctionsKuppusamy P User defined methods in Java allow programmers to define reusable blocks of code to perform operations or calculations. There are different ways to define methods based on whether they return a value and whether they accept parameters. Methods can be called within other parts of the code to execute the block of statements defined within the method. Some key points about methods in Java include:
- Methods can be defined within classes and allow for code reusability.
- The syntax for defining a method includes modifiers, return type, name, parameters, and a block of statements.
- Methods can be overloaded by having multiple methods with the same name but different parameters. This improves readability.
- Recursion is when a method calls
Java arrays
Java arraysKuppusamy P Access modifiers in Java define the scope of variables, methods, and classes. The four access modifiers are public, private, protected, and default. Public members are accessible everywhere, private within the class, protected within the package and subclasses, and default within the package. Arrays are objects that hold multiple values of the same type. One-dimensional arrays have a fixed length, and elements are initialized with default values. Multi-dimensional arrays are arrays of arrays. Two-dimensional arrays represent rows and columns. Array bounds cannot be exceeded, but the reference variable can refer to a new array. The arraycopy method copies arrays.
Java iterative statements
Java iterative statementsKuppusamy P The document discusses different types of iterative statements in Java including while, do-while, for, enhanced for loops, and the continue statement. It provides the syntax and examples of each statement type. The while loop executes statements until a condition is false. The do-while loop executes statements once, then checks the condition. The for loop allows initialization, condition checking, and increment/decrement in the header. The enhanced for loop iterates over arrays or collections. The continue statement skips the current iteration in a loop.
Java conditional statements
Java conditional statementsKuppusamy P Conditional statements in Java include if-else statements, nested if-else statements, and switch statements. If-else statements execute code based on a boolean condition, while switch statements allow testing multiple conditions. Type conversion in Java includes widening (automatic) conversions between compatible types like int to double, and narrowing (manual) conversions between incompatible types using explicit casting like double to int. Methods like parseInt() allow converting between types like String to int.
Java data types
Java data typesKuppusamy P This document discusses variables and data types in Java. It covers Java packages, APIs, features like object-oriented programming, and advantages like security and portability. It also discusses the different types of variables in Java like local, instance, and static variables. The various operators in Java are presented, including arithmetic, relational, logical, assignment, shift, and bitwise operators. Code examples are provided to demonstrate the usage of these operators.
Java introduction
Java introductionKuppusamy P Java Introduction, Evolution of Java, Java Architecture, Syntax of Java Program, Simple Java Program, Java Naming Convention, Java Comments
Logistic regression in Machine Learning
Logistic regression in Machine LearningKuppusamy P Logistic regression is a predictive analysis algorithm that can be used for classification problems. It estimates the probabilities of different classes using the logistic function, which outputs values between 0 and 1. Logistic regression transforms its output using the sigmoid function to return a probability value. It is used for problems like email spam detection, fraud detection, and tumor classification. The independent variables should be independent of each other and the dependent variable must be categorical. Gradient descent is used to minimize the loss function and optimize the model parameters during training.
Anomaly detection (Unsupervised Learning) in Machine Learning
Anomaly detection (Unsupervised Learning) in Machine LearningKuppusamy P Anomaly detection techniques are used to identify rare items, events or observations which raise suspicions by differing significantly from the majority of the data. There are various types of anomalies including point anomalies, contextual anomalies and collective anomalies. Anomaly detection algorithms typically build a model of normal behavior and then label new data as normal or anomalous based on how well it fits the model. Common techniques include clustering, statistical methods and distance-based approaches. Applications include fraud detection, system failure diagnosis and cybersecurity.
Machine Learning Performance metrics for classification
Machine Learning Performance metrics for classificationKuppusamy P The document discusses different performance metrics for evaluating outlier detection models: recall, false alarm rate, ROC curves, and AUC. ROC curves plot the true positive rate against the false positive rate. AUC measures the entire area under the ROC curve, indicating how well a model can distinguish between classes. F1 score provides a balanced measure of a model's precision and recall that is better than accuracy alone when classes are unevenly distributed. Accuracy can be high even when a model misses many true outliers, so F1 score is a more appropriate metric for outlier detection evaluation.
Machine learning Introduction
Machine learning IntroductionKuppusamy P Machine learning is a form of artificial intelligence that allows systems to learn from data and improve automatically without being explicitly programmed. It works by building mathematical models based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Linear regression is a commonly used machine learning algorithm that allows predicting a dependent variable from an independent variable by finding the best fit line through the data points. It works by minimizing the sum of squared differences between the actual and predicted values of the dependent variable. Gradient descent is an optimization algorithm used to train machine learning models by minimizing a cost function relating predictions to ground truths.
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Algorithm basics
- 1. Algorithm Dr. Kuppusamy .P Associate Professor / SCOPE
- 2. Algorithm A finite set of statements that guarantees an optimal solution for logical and mathematical problems in finite interval of time. Example Problem : Average of 2 numbers Algorithm Source Code in ‘C’ 1. Declare Variables int a,b, add; 2. Get input scanf(“%d %d” &a, &b); 3. Find average and assign the value average= (a+b)/2; 4. Display output result printf(“%d”,average); No. of Statements = 4 Optimal Solution = average of 2 numbers Dr. Kuppusamy P
- 3. Characteristics of Algorithm • Input: Zero or more quantities which are externally supplied. • Output: At least one quantity is generated. • Finiteness: Execution terminates after a finite number of steps for all possible inputs. • Definiteness: Each step must be clear and unambiguous. • Effectiveness: Every step must be given enough information to produce results. Dr. Kuppusamy P
- 4. Measure the performance of Algorithms • Time Complexity • Space Complexity - Computational resources (time complexity) is usually more important than space complexity Good Algorithms Should: ▪ Run in less time i.e. Less Time Complexity ▪ Consume less memory i.e. Less Space Complexity Dr. Kuppusamy P
- 5. References Dr. Kuppusamy P • Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, “Introduction to Algorithms”, MIT Press.