WHAT ARE DIFFERENT TECHNIQUES FOR MAKING HASH FUNCTION? Techniques for making hash function. • Truncation Method This is the simplest method for computing address from a key.In this method we take only a part of the key as address. • Midsquare Method In this method the key is squared and some digits from the middle of this square are taken as address. • Folding Method In this technique, the key is divided into different part where the length of each part is same as that of the required address, except possibly the last part. • Division Method (Modulo-Division) In Modulo-Division method the key is divided by the table size and the remainder is taken as the address of the hash table. –>Let the table size is n then H (k) =k mod n Master C and Data Structure, in this C and Data Structure certification training. WHAT ARE THE ISSUES THAT HAMPER THE EFFICIENCY IN SORTING A FILE? The issues are: Length of time required by the programmer i...
WHAT IS ARTIFICIAL INTELLIGENCE? Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans. WHAT IS AN ARTIFICIAL INTELLIGENCE NEURAL NETWORKS? Artificial intelligence Neural Networks can model mathematically the way biological brain works, allowing the machine to think and learn the same way the humans do- making them capable of recognizing things like speech, objects and animals like we do. WHAT ARE THE VARIOUS AREAS WHERE AI (ARTIFICIAL INTELLIGENCE) CAN BE USED? Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc. WHICH IS NOT COMMONLY USED PROGRAMMING LANGUAGE FOR AI? Perl language is not commonly used programming language for AI WHAT IS PROLOG IN AI? In AI, Prolog is a programming language based on logic. GIVE AN EXPLANATION ON THE DIFFERENCE B...
CHAPTER 1:INTRODUCTION In this course you will learn to implement mathematical ideas in machine learning. You will investigate the process of learning and understand the application of various learning algorithms. Prerequisites: The courses assignments and notes will use python programming language and expects a basic knowledge of python. We assume the student has completed the Machine Learning Foundations or has an equivalent fluency in mathematics and fundamentals. CHAPTER 2: LINEAR MODELS Understand linear approximation and modelling of problems and develop linear models CHAPTER 3: DIMENSIONALITY REDUCTION Use ideas from linear algebra to transform dimensions and warp space providing additional flexibility and functionality to linear models. CHAPTER 4:SVM Develop and implement kernel based methods to develop nonlinear models to solve few complex tasks. CHAPTER 5:NEAREST NEIGHBOURS, K-MEANS, AND GAUSSIAN MIXTURE MODELS Review pattern recognition ideas with ...
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