JNTUK R16 4-2 Machine Learning Material/Notes PDF Download

JNTUK R16 4-2 Machine Learning Material/Notes PDF Download

Students those who are studying JNTUK R16 CSE Branch, Can Download Unit wise R16 4-2 Machine Learning Material/Notes PDFs below.

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JNTUK R16 4-2 Machine Learning Material/Notes PDF Download

OBJECTIVES:

  • Familiarity with a set of well-known supervised, unsupervised and semi-supervised
  • learning algorithms.
  • The ability to implement some basic machine learning algorithms
  • Understanding of how machine learning algorithms are evaluated

UNIT-1

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

Download UNIT-1 Material PDF | Reference-2 | Ref-3 | Ref-4

UNIT-2

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

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UNIT-3

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

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UNIT-4:

Linear models: The least-squares method, The perceptron: a heuristic learning algorithm for linear classifiers, Support vector machines, obtaining probabilities from linear classifiers, Going beyond linearity with kernel methods.Distance Based Models: Introduction, Neighbours and exemplars, Nearest Neighbours classification, Distance Based Clustering, Hierarchical Clustering.

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UNIT-5

Probabilistic models: The normal distribution and its geometric interpretations, Probabilistic models for categorical data, Discriminative learning by optimising conditional likelihoodProbabilistic models with hidden variables.Features: Kinds of feature, Feature transformations, Feature construction and selection. Model ensembles: Bagging and random forests, Boosting

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UNIT-6

Dimensionality Reduction: Principal Component Analysis (PCA), Implementation and demonstration. Artificial Neural Networks:Introduction, Neural network representation, appropriate problems for neural network learning, Multilayer networks and the back propagation algorithm.

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TEXT BOOKS:

  1. Machine Learning: The art and science of algorithms that make sense of data, Peter Flach, Cambridge.
  2. Machine Learning, Tom M. Mitchell, MGH.

REFERENCE BOOKS:

  1. UnderstandingMachine Learning: From Theory toAlgorithms, Shai Shalev-Shwartz, Shai BenDavid, Cambridge.
  2. Machine Learning in Action, Peter Harington, 2012, Cengage.

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.

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