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
本课程是 Data Science at Scale 专项课程 专项课程的一部分
Practical Predictive Analytics: Models and Methods
提供方
教学大纲 - 您将从这门课程中学到什么
周
1Practical 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.
28 个视频 （总计 121 分钟）
28 个视频
Hypothesis Testing5分钟
Significance Tests and P-Values3分钟
Example: Difference of Means4分钟
Deriving the Sampling Distribution6分钟
Shuffle Test for Significance4分钟
Comparing Classical and Resampling Methods3分钟
Bootstrap6分钟
Resampling Caveats6分钟
Outliers and Rank Transformation3分钟
Example: Chi-Squared Test3分钟
Bad Science Revisited: Publication Bias4分钟
Effect Size4分钟
Meta-analysis5分钟
Fraud and Benford's Law4分钟
Intuition for Benford's Law2分钟
Benford's Law Explained Visually3分钟
Multiple Hypothesis Testing: Bonferroni and Sidak Corrections3分钟
Multiple Hypothesis Testing: False Discovery Rate4分钟
Multiple Hypothesis Testing: Benjamini-Hochberg Procedure3分钟
Big Data and Spurious Correlations4分钟
Spurious Correlations: Stock Price Example3分钟
How is Big Data Different?3分钟
Bayesian vs. Frequentist4分钟
Motivation for Bayesian Approaches3分钟
Bayes' Theorem2分钟
Applying Bayes' Theorem4分钟
Naive Bayes: Spam Filtering4分钟
周
2Supervised Learning
Follow a tour through the important methods, algorithms, and techniques in machine learning. You will learn how these methods build upon each other and can be combined into practical algorithms that perform well on a variety of tasks. Learn how to evaluate machine learning methods and the pitfalls to avoid.
26 个视频 （总计 111 分钟）, 1 个阅读材料, 1 个测验
26 个视频
Simple Examples3分钟
Structure of a Machine Learning Problem5分钟
Classification with Simple Rules5分钟
Learning Rules4分钟
Rules: Sequential Covering3分钟
Rules Recap2分钟
From Rules to Trees2分钟
Entropy4分钟
Measuring Entropy4分钟
Using Information Gain to Build Trees6分钟
Building Trees: ID3 Algorithm2分钟
Building Trees: C.45 Algorithm4分钟
Rules and Trees Recap3分钟
Overfitting7分钟
Evaluation: Leave One Out Cross Validation5分钟
Evaluation: Accuracy and ROC Curves5分钟
Bootstrap Revisited4分钟
Ensembles, Bagging, Boosting4分钟
Boosting Walkthrough5分钟
Random Forests3分钟
Random Forests: Variable Importance5分钟
Summary: Trees and Forests2分钟
Nearest Neighbor4分钟
Nearest Neighbor: Similarity Functions4分钟
Nearest Neighbor: Curse of Dimensionality3分钟
1 个阅读材料
R Assignment: Classification of Ocean Microbes10分钟
1 个练习
R Assignment: Classification of Ocean Microbes28分钟
周
3Optimization
You will learn how to optimize a cost function using gradient descent, including popular variants that use randomization and parallelization to improve performance. You will gain an intuition for popular methods used in practice and see how similar they are fundamentally.
11 个视频 （总计 41 分钟）
11 个视频
Gradient Descent Visually4分钟
Gradient Descent in Detail2分钟
Gradient Descent: Questions to Consider3分钟
Intuition for Logistic Regression4分钟
Intuition for Support Vector Machines3分钟
Support Vector Machine Example3分钟
Intuition for Regularization3分钟
Intuition for LASSO and Ridge Regression3分钟
Stochastic and Batched Gradient Descent5分钟
Parallelizing Gradient Descent3分钟
周
4Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
4 个视频 （总计 21 分钟）, 1 个测验
4 个视频
K-means5分钟
DBSCAN4分钟
DBSCAN Variable Density and Parallel Algorithms4分钟
4.1
50 个审阅33%
完成这些课程后已开始新的职业生涯
25%
通过此课程获得实实在在的工作福利
热门审阅
创建者 SP•Dec 23rd 2016
Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.
创建者 KP•Feb 8th 2016
I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .
讲师
Bill Howe
Director of ResearchScalable Data Analytics
关于 University of Washington
Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.
关于 Data Science at Scale 专项课程
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
This Specialization covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project.
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