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.
提供方
大规模数据科学 专项课程华盛顿大学课程信息
9,577 次近期查看
学生职业成果
25%
完成这些课程后已开始新的职业生涯
20%
通过此课程获得实实在在的工作福利
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 4 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
完成时间大约为7 小时
英语（English）
字幕：法语（French）, 巴西葡萄牙语, 韩语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
Random ForestPredictive AnalyticsMachine LearningR Programming
学生职业成果
25%
完成这些课程后已开始新的职业生涯
20%
通过此课程获得实实在在的工作福利
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 4 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
完成时间大约为7 小时
英语（English）
字幕：法语（French）, 巴西葡萄牙语, 韩语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
提供方
华盛顿大学
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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 2 小时
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.
完成时间为 2 小时
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
完成时间为 3 小时
Supervised 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.
完成时间为 3 小时
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 Microbes30分钟
完成时间为 1 小时
Optimization
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.
完成时间为 1 小时
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
完成时间为 1 小时
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
完成时间为 1 小时
4 个视频 （总计 21 分钟）
4 个视频
K-means5分钟
DBSCAN4分钟
DBSCAN Variable Density and Parallel Algorithms4分钟
审阅
来自实用预测分析：模型与方法的热门评论
由 NE 提供Jun 7, 2017
I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.
由 SP 提供Dec 22, 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.
由 WK 提供Jun 5, 2017
Excellent Lectures. Since the course is several years old the organization of some of the assignments needs updating. That's the only reason I gave it 4 instead of 5 stars.
由 KP 提供Feb 7, 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 .
关于 大规模数据科学 专项课程
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
常见问题
我什么时候能够访问课程视频和作业？
我订阅此专项课程后会得到什么？
Is financial aid available?
完成课程后，我会获得大学学分吗？
还有其他问题吗？请访问 学生帮助中心。
