Greedy algorithm
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A greedy algorithm makes locally optimal choices at each step in the hope of reaching a globally optimal solution. It works well when the problem has properties of optimal substructure and a greedy choice leads to an optimal solution. Huffman coding is an example of where a greedy approach succeeds - it assigns variable length codes to symbols based on frequency, with more common symbols getting shorter codes, in a way that minimizes the overall code length.
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The document discusses the main functions of a data manipulation language (DML) which allow users to manipulate data in a database. DML commands include SELECT to retrieve data, INSERT to add new rows to tables, UPDATE to modify existing data, and DELETE to remove rows from tables. Examples of SQL syntax are provided for each DML command including SELECT, INSERT, UPDATE, and DELETE.
DDL DATA DEFINATION LANGUAGE
DDL(Data definition language) is a syntax for defining data structures like databases. Common DDL statements are CREATE, ALTER, RENAME, DROP AND TRUNCATE. The CREATE TABLE statement is used to create a new table with columns and datatypes. The ALTER TABLE statement modifies existing tables by adding, deleting, or changing columns. The RENAME statement renames existing tables, while DROP TABLE removes tables and TRUNCATE TABLE empties table contents but keeps the table.
Java
Java is an object-oriented programming language that can create applications that run on multiple platforms. When a Java program is compiled, it produces bytecode that can run on any system with a Java Virtual Machine. The file must contain a public class that matches the file name and can include import statements for external libraries.
Weka presentation
Weka is a machine learning software and data mining tool developed at the University of Waikato, New Zealand. It provides algorithms for data preprocessing, classification, clustering, attribute selection, and association rule mining. Weka contains tools for data preprocessing, classification, clustering, visualization, and feature selection. It has a graphical user interface and can be used for classification, regression, clustering, association rules, and visualization.
Machine learning
This document provides an overview of machine learning. It discusses the history of machine learning beginning in 1957 with the first neural network. It defines machine learning as using algorithms and statistical models to perform tasks without explicit instructions by learning from patterns in data. Supervised learning uses labeled training data to guide model training, and is used for classification and regression problems. Unsupervised learning finds patterns in unlabeled data using clustering. Reinforcement learning involves an agent interacting with an environment and receiving rewards or penalties to learn the best outcomes. Popular machine learning software libraries and applications are also mentioned.
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Greedy algorithm
- 2. WHAT IS GREEDY ALGORITHM? A greedy algorithm, as the name suggests, always makes the choice that seems to be the best at that moment. This means that it makes a locally-optimal choice in the hope that this choice will lead to a globally-optimal solution. Greedy algorithms (This is not an algorithm, it is a technique.) However, in many problems, a greedy strategy does not produce an optimal solution.
- 5. PROPERTIES If both of the properties below are true, a greedy algorithm can be used to solve the problem. Greedy choice property: A global (overall) optimal solution can be reached by choosing the optimal choice at each step. Optimal substructure: A problem has an optimal substructure if an optimal solution to the entire problem contains the optimal solutions to the sub-problems.
- 8. HUFFMAN CODING is another example of an algorithm where a greedy approach is successful. The Huffman algorithm analyzes a message and depending on the frequencies of the characters used in the message, it assigns a variable-length encoding for each symbol. A more commonly used symbol will have a shorter encoding while a rare symbol will have a longer encoding. The Huffman coding algorithm takes in information about the frequencies or probabilities of a particular symbol occurring. It begins to build the prefix tree from the bottom up, starting with the two least probable symbols in the list. It takes those symbols and forms a subtree containing them, and then removes the individual symbols from the list. The algorithm sums the probabilities of elements in a subtree and adds the subtree and its probability to the list. Next, the algorithm searches the list and selects the two symbols or subtrees with the smallest probabilities. It uses those to make a new subtree, removes the original subtrees/symbols from the list, and then adds the new subtree and its combined probability to the list. This repeats until there is one tree and all elements have been added. At each subtree, the optimal encoding for each symbol is created and together composes the overall optimal encoding.