0

(

ratings

)

22

students

Created by:

Profile Photo

Last updated:

May 17, 2022

Duration:

6 months

FREE

This course includes:

6 months

Badge on Completion

Certificate of completion

6 months

Description

Introduces the theory and application of modern, computationally-based methods for exploring and drawing inferences from data. Covers re-sampling methods, non-parametric regression, prediction, and dimension reduction and clustering. Specific topics include Monte Carlo simulation, bootstrap cross-validation, splines, locally weighted regression, CART, random forests, neural networks, support vector machines, and hierarchical clustering. De-emphasizes proofs and replaces them with an extended discussion of the interpretation of results and simulation and data analysis for illustration.

Course Objectives

After completing this course, a student will be able to understand the theoretical basis for the current methods used in statistical analysis.

Readings

  • T. Hastie, R. Tibshirani, and J. H. Fried. (2001) The Elements of Statistical Learning. Springer-Verlag: New York.
  • Venables, W.N. and Ripley, B.D. (2002) Modern Applied Statistics with S-Plus. Springer-Verlag: New York.
  • Brian D. Ripley. (1996) Pattern Recognition and Neural Networks. Cambridge University Press.

Course Curriculum

    • Stuff You Should Know: Basics of Probability, the Central Limit Theorem, and Inference 00:25:00
    • Introduction to Regression and Prediction 00:55:00
    • Overview of Supervised Learning 00:55:00
    • Linear Methods for Regression 00:55:00
    • Linear Methods for Classification 00:55:00
    • Kernel Methods 00:55:00

About the instructor

5 5

Instructor Rating

6

Reviews

4637

Courses

24151

Students

Profile Photo
OpenCoursa
We are an educational and skills marketplace to accommodate the needs of skills enhancement and free equal education across the globe to the millions. We are bringing courses and trainings every single day for our users. We welcome everyone woth all ages, all background to learn. There is so much available to learn and deliver to the people.