Department of MathematicslabSem 5
MACHINE LEARNING LAB
DSE 2244
Syllabus
- 01Tutorial on tools for Machine Learning
- 02Python scripting (suggested)
- 03Experiments with datasets to perform preprocessing
- 04Deploy classifiers: Bayesian, Decision Trees, Support Vector Machines, k-nearest neighbor, Regression Models
- 05Classification accuracy measures
- 06Improving classifier performance through ensembling, boosting etc.
References
- Hans Peter Langtangen, Python Scripting for Computational Science, (3e), Springer Publishers, 2014
- Naomi R. Ceder, The Quick Python Book, (2e), Manning Publications Co., 2010
- Wesley J. Chun, Core Python Applications Programming, (3e), Prentice Hall Publishers, 2012
- G. James, D. Witten, T Hastie, R Tibshirani, An introduction to statistical learning with applications in R, Springer, 2013
Credits Structure
0Lecture
0Tutorial
3Practical
2Total