Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection
Multiple Hypothesis Testing: Bonferroni and Sidak Corrections
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Получаемые навыки
Random Forest, Predictive Analytics, Machine Learning, R Programming
Рецензии
PV
Nov 12, 2015
The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course
CC
Jul 20, 2016
Nive that the course covered a broad range of topics.\n\nAnd good to get pushed to do some kaggle competition and peer review.
Из урока
Practical Statistical Inference
Learn the basics of statistical inference, comparing classical methods with resampling methods that allow you to use a simple program to make a rigorous statistical argument. Motivate your study with current topics at the foundations of science: publication bias and reproducibility.
Преподаватели
Bill Howe
Director of Research