Machine Learning: Multi-class Classification
Posts
ML Objectives and Outcomes
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 Syllabus
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 Questions Set-I
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.?