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What is the relation between statistical inference and probability? Statistical inference uses data analysis to presume properties of any probability distribution.
This course will introduce you to the theory and application of modern, computationally-based methods for exploring and drawing inferences from data. You will get the knowledge of re-sampling methods, non-parametric regression, prediction, and dimension reduction and clustering. Specific topics include Monte Carlo simulation, bootstrap cross-validation, splines, local weighted regression, CART, neural networks and so on.
Assessment
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Certification
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Course Credit: JHSPH Open
Course Curriculum
Review Stuff You Should Know Basics of Probability, the Central Limit Theorem, and Inference | 00:05:00 | ||
Lecture 1 Introduction to Regression and Prediction | 00:10:00 | ||
Lecture 2 Overview of Supervised Learning | 00:25:00 | ||
Lecture 3 & 4 Linear Methods for Regression | 00:20:00 | ||
Lecture 5 Linear Methods for Classification | 00:26:00 | ||
Lecture 6 Kernel Methods | 00:25:00 | ||
Assessment | |||
Submit Your Assignment | 00:00:00 | ||
Certification | 00:00:00 |
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