Computer Science Tutorials

MACHINE LEARNING OBJECTIVES: Familiarity with a set of well-known supervised, unsupervised and semi-supervise   Learning algorithms.   The ability to implement some basic machine learning algorithm. Understanding of how machine learning algorithms are evaluated. OUTCOMES: Recognize the characteristics of machine learning that make it useful to real-world Problems. Characterize machine-learning algorithms as supervised, semi-supervised, and unsupervised. Have heard of a few machine learning toolboxes. Be able to use support vector machines. Be able to use regularized regression algorithms. Understand the concept behind neural networks for learning non-linear functions.

MACHINE LEARNING UNIT -I: The ingredients of machine learning, Tasks: the problems that can be solved with machine learning, Models: the output of machine learning, Features, the workhorses of machine learning. Binary classification and related tasks: Classification, Scoring and ranking, Class probability estimation UNIT-II: Beyond binary classification:  Handling more than two classes, Regression, Unsupervised and descriptive learning. Concept learning : The hypothesis space, Paths through the hypothesis space, Beyond conjunctive concepts UNIT- III: Tree models: Decision trees, Ranking and probability estimation trees, Tree learning as variance reduction. Rule models: Learning ordered rule lists, Learning unordered rule sets, Descriptive rule learning, First-order rule learning UNIT-IV: Linear models: The least-squares method, The perceptron: a heuristic learning algorithm for linear classifiers, Support vector machines, obtaining probabilities from linear cla...

Assignment I 1. What is meant by Data mining? Explain with neat sketch of Knowledge Discovery from Data? 2. Discuss about major issues in data mining? 3. Explain what kind of data can be mined? ======================================================================== Assignment II 1. What is meant by data pre-processing? Explain with major Tasks in Data pre-processing? 2. What is lossless and lossy dimensionality reduction? Describe any one technique for lossy dimensionality reduction? 3. Explain the data cleaning Techniques? ======================================================================== Assignment III 1. Define Classification? Explain the General approach to solving a classification problem? 2. Explain the methods for expressing an attribute set conditions? 3. Discuss various measures to selecting the best split.?

DATA WAREHOUSING AND DATA MINING UNIT –I: Introduction: Why Data Mining? What Is Data Mining? What Kinds of Data Can Be Mined? What Kinds of Patterns Can Be Mined? Which Technologies Are Used? Which Kinds of Applications Are Targeted? Major Issues in Data Mining. Data Objects and Attribute Types, Basic Statistical Descriptions of Data, Data Visualization, Measuring Data Similarity and Dissimilarity UNIT –II: Data Pre-processing: Data Preprocessing: An Overview, Data Cleaning, Data Integration, Data Reduction, Data Transformation, and Data Discretization UNIT –III: Classification: Basic Concepts, General Approach to solving a classification problem, Decision Tree Induction: Working of Decision Tree, building a decision tree, methods for expressing an attribute test condition, measures for selecting the best split, Algorithm for decision tree induction. UNIT –IV: Classification: Alternative Techniques, Bayes’ Theorem, Naïve Bayesian Classification, Bayesian Belief Networks...

DATA MINING As Per the JNTU Kakinada Syllabus, tutorials will be available here S.No Description Download Links 1. Data Warehousing and Data Mining Syllabus     Click Here 2. DWDM Text Book     Click Here 3. Data Mining  Material Unit -1     Click Here Unit -2      Click Here Unit -3    Click Here Unit -4    Click Here Unit -5    Click Here Unit -6    Click Here 4. Data Mining Previous Question Papers 2016     Click Here   DWDM Powerpoint Presentation             Unit- 1                                                                Click Here             ...