关于此课程： 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
制作方： 华盛顿大学
教学方： Bill Howe, Director of Research
Scalable Data Analytics
| 基本信息 | 课程 2（共 4 门，Data Science at Scale Specialization ） |
| 承诺学习时间 | 4 weeks of study, 6-8 hours/week |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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.
28 视频
- 视频: Appetite Whetting: Bad Science
- 视频: Hypothesis Testing
- 视频: Significance Tests and P-Values
- 视频: Example: Difference of Means
- 视频: Deriving the Sampling Distribution
- 视频: Shuffle Test for Significance
- 视频: Comparing Classical and Resampling Methods
- 视频: Bootstrap
- 视频: Resampling Caveats
- 视频: Outliers and Rank Transformation
- 视频: Example: Chi-Squared Test
- 视频: Bad Science Revisited: Publication Bias
- 视频: Effect Size
- 视频: Meta-analysis
- 视频: Fraud and Benford's Law
- 视频: Intuition for Benford's Law
- 视频: Benford's Law Explained Visually
- 视频: Multiple Hypothesis Testing: Bonferroni and Sidak Corrections
- 视频: Multiple Hypothesis Testing: False Discovery Rate
- 视频: Multiple Hypothesis Testing: Benjamini-Hochberg Procedure
- 视频: Big Data and Spurious Correlations
- 视频: Spurious Correlations: Stock Price Example
- 视频: How is Big Data Different?
- 视频: Bayesian vs. Frequentist
- 视频: Motivation for Bayesian Approaches
- 视频: Bayes' Theorem
- 视频: Applying Bayes' Theorem
- 视频: Naive Bayes: Spam Filtering
第 2 周
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.
26 视频, 1 阅读材料
- 视频: Statistics vs. Machine Learning
- 视频: Simple Examples
- 视频: Structure of a Machine Learning Problem
- 视频: Classification with Simple Rules
- 视频: Learning Rules
- 视频: Rules: Sequential Covering
- 视频: Rules Recap
- 视频: From Rules to Trees
- 视频: Entropy
- 视频: Measuring Entropy
- 视频: Using Information Gain to Build Trees
- 视频: Building Trees: ID3 Algorithm
- 视频: Building Trees: C.45 Algorithm
- 视频: Rules and Trees Recap
- 视频: Overfitting
- 视频: Evaluation: Leave One Out Cross Validation
- 视频: Evaluation: Accuracy and ROC Curves
- 视频: Bootstrap Revisited
- 视频: Ensembles, Bagging, Boosting
- 视频: Boosting Walkthrough
- 视频: Random Forests
- 视频: Random Forests: Variable Importance
- 视频: Summary: Trees and Forests
- 视频: Nearest Neighbor
- 视频: Nearest Neighbor: Similarity Functions
- 视频: Nearest Neighbor: Curse of Dimensionality
- Reading: R Assignment: Classification of Ocean Microbes
已评分: R Assignment: Classification of Ocean Microbes
第 3 周
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.
11 视频
- 视频: Optimization by Gradient Descent
- 视频: Gradient Descent Visually
- 视频: Gradient Descent in Detail
- 视频: Gradient Descent: Questions to Consider
- 视频: Intuition for Logistic Regression
- 视频: Intuition for Support Vector Machines
- 视频: Support Vector Machine Example
- 视频: Intuition for Regularization
- 视频: Intuition for LASSO and Ridge Regression
- 视频: Stochastic and Batched Gradient Descent
- 视频: Parallelizing Gradient Descent
第 4 周
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
4 视频
- 视频: Introduction to Unsupervised Learning
- 视频: K-means
- 视频: DBSCAN
- 视频: DBSCAN Variable Density and Parallel Algorithms
已评分: Kaggle Competition Peer Review
常见问题解答
运作方式
Coursework
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制作方
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.
价格
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|---|---|
| 访问课程材料 | 可用 |
| 访问评分的材料 | 可用 |
| 收到最终成绩 | 可用 |
| 获得可共享的课程证书 | 可用 |
评分和审阅
Excellent!!
Very good approach to each method; the assignments are a good test for the topics.
great for learner
rather nice course. learn R before joining