Back close

Course Detail

Course Name Computational Statistics and Inference Theory
Course Code 18CS709
Credits Coimbatore
Year Taught 2018


Course Syllabus

Computational Statistics- Probability concepts, Sampling Concepts, Generating Random Variables, Exploratory Data Analysis, Monte Carlo Methods for Inferential Statistics, Data Partitioning, Probability Density Estimation, Statistical Pattern Recognition, Nonparametric Regression. Data Mining- data mining algorithms-Instance and Features, Types of Features (data), Concept Learning and Concept Description, Output of data mining Knowledge Representation; Decision Trees- Classification and Regression trees constructing.

Classification trees, Algorithm for Normal Attributes, Information Theory and Information. Entropy, Building tree, Highly-Branching Attributes, ID3 to c4.5, CHAID, CART, Regression Trees, Model Trees, Pruning. Preprocessing and Post processing in data mining – Steps in Preprocessing, Discretization, Manual Approach, Binning, Entropy- based Discretization, Gaussian Approximation, K-tile method, Chi Merge, Feature extraction, selection and construction, Feature extraction, Algorithms, Feature selection, Feature construction, Missing Data, Post processing. Association Rule Mining- The Apriori Algorithm. Multiple Regression Analysis, Logistic Regression, k- Nearest Neighbor Classification, Constructing new attributes for algorithms of decision trees. Induction, Quick, Unbiased and Efficient Statistical tree.

Course Outcome

At the end of the course the students will be able to

Course Outcome Bloom’s Taxonomy Level
CO 1 Concepts of probability and statistics L2
CO 2 Data analysis, statistical pattern recognition and data mining concepts L2
CO 3 Classification and regression trees L3
CO 4 Multiple Regression Analysis, Logistic Regression, k- Nearest Neighbor Classification L3
CO 5 Feature selection and extraction algorithms L4
CO 6 Implementations of Logistic regression and Apriori algorithms L5

Text Books / References

  1. Wendy L. Martinez and Angel R, “Martinez Computational Statistics,” Chapman & Hall/CRC, 2002.
  2. Ian H. Witten, “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations”, Morgan Kaufmann, 2000.
  3. Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann Publishers, 2001.
  4. K. P. Soman, V. Ajay and Diwakar Shyam, “Insight into Data Mining: Theory and Practice”, Prentice Hall India, 2005.


‘Computational Statistics and Inference Theory’ is an elective course offered in M. Tech. in Computer Science and Engineering program at School of Engineering, Amrita Vishwa Vidyapeetham.

DISCLAIMER: The appearance of external links on this web site does not constitute endorsement by the School of Biotechnology/Amrita Vishwa Vidyapeetham or the information, products or services contained therein. For other than authorized activities, the Amrita Vishwa Vidyapeetham does not exercise any editorial control over the information you may find at these locations. These links are provided consistent with the stated purpose of this web site.

Admissions Apply Now