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Course Detail

Course Name Machine Learning and Artificial Intelligence
Course Code 19CCE213
Program B. Tech. in Computer and Communication Engineering
Semester Four
Year Taught 2019


Unit 1

Measuring the central tendency – measuring the dispersion of data – graphic displays of basic descriptive data Summaries – Missing values – noisy data- data cleaning as a process – Data integration – data transformation – Data cube aggregation – attribute subset selection – dimensionality reduction.

Unit 2

Cluster Analysis using k–Means – k–Medoids – single linkage – complete linkage – UPGMA and expectation Maximization – Assessing clustering tendency – determining the number of clusters – measuring clustering quality k– nearest neighbor – Bayes – decision tree and Support Vector Machines (SVM) classifiers – Classifier accuracy Measures – evaluating the accuracy of a Classifier.

Unit 3

Efficient and Scalable Frequent Itemset Mining Methods- Mining Various Kinds of Association Rules- From Association Mining to Correlation Analysis- Constraint-Based Association Mining.

Lab Component

Experiments on machine learning and artificial intelligence algorithms using Matlab / Python.


  • JiaweiHan ,MichelineKamber , Jian Pei , “Data Mining : Concepts and Techniques”, 3rd Edition, Morgan Kaufmann Publishers (Elsevier), 2011.
  • K.P Soman, R. Loganathan , V. Ajay, “Machine Learning with SVM and other Kernel Methods”, PHI Learning Private Ltd., New Delhi, 2009.


  • Earl Gose, RichardJohnsonbaugh, Steve Jost, “Pattern Recognition and Image Analysis”, Pearson Education India, 2015.
  • Christopher Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.

Evaluation Pattern

Assessment Internal External
Periodical 1 15
Periodical 2 15
*Continuous Assessment (CAT) 20
*Continuous Assessment (CAL) 30
End Semester 35
*CA – Can be Quizzes, Assignment, Projects, and Reports.

Objectives and Outcomes


  • To introduce mathematical methods for design of machine learning algorithms
  • To provide an overview of cluster analysis process and cluster quality evaluation techniques
  • To enable design and performance evaluation of classifiers for typical classification problems
  • To enable design of frequent itemsetmining system for typical solve market-basket analysis problems

Course Outcomes

  • CO1: Able to generate, analyze and interpret data summaries
  • CO2: Able to carry out analysison machine learning algorithms
  • CO3: Able to design and implement classifiers for machine learning applications
  • CO4: Able to design and implement frequent itemset mining systems

CO – PO Mapping

CO1 3 3
CO2 3 2 2 3
CO3 2 3 2 2 3
CO4 2 3 2 2 3

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