Page 78 - REC :: All Dept Open Electives

TEXT BOOKS:

1. ―Machine Learning with R‖, Brett Lantz, Packt Publishing, First Edition, 2013.
2. ―Beginning R: The Statistical Programming Language‖ , Mark Gardener, Wrox Wiley
Publication, First Edition, 2012.
3. ―R for Everyone:Advanced Analytics and Graphics‖,Jared P.Lander,Pearson Education,2015.

REFERENCES:

1. Nina Zumel, John Mount, ―Practical Data Science with R‖, Manning Publications, 2014.
2. http://www.johndcook.com/R_language_for_programmers.html.
3. W. N. Venables, D. M. Smith and the R Core Team, ―An Introduction to R‖, 2013.
4. Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta, ―Practical Data
Science Cookbook‖, Packt Publishing Ltd., 2014.

OUTCOMES:

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

1. Understand the applications and uses of machine learning
2. Apply basic constructs in R
3. Apply machine learning by various classification techniques
4. Apply market basket analysis and clustering techniques
5. Evaluate the performance of the models built and fine tune the models to improve them

Curriculum and Syllabus | Open Electives | R 2017 | REC Page 78