À propos de ce cours : 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
Créé par : University of Washington
Enseigné par : Bill Howe, Director of Research
Scalable Data Analytics
| Informations de base | Cours 2 de 4 dans Data Science at Scale Specialization |
| Engagement | 4 weeks of study, 6-8 hours/week |
| Langue | English, Sous-titres : Korean |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
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 vidéos
- Vidéo: Hypothesis Testing
- Vidéo: Significance Tests and P-Values
- Vidéo: Example: Difference of Means
- Vidéo: Deriving the Sampling Distribution
- Vidéo: Shuffle Test for Significance
- Vidéo: Comparing Classical and Resampling Methods
- Vidéo: Bootstrap
- Vidéo: Resampling Caveats
- Vidéo: Outliers and Rank Transformation
- Vidéo: Example: Chi-Squared Test
- Vidéo: Bad Science Revisited: Publication Bias
- Vidéo: Effect Size
- Vidéo: Meta-analysis
- Vidéo: Fraud and Benford's Law
- Vidéo: Intuition for Benford's Law
- Vidéo: Benford's Law Explained Visually
- Vidéo: Multiple Hypothesis Testing: Bonferroni and Sidak Corrections
- Vidéo: Multiple Hypothesis Testing: False Discovery Rate
- Vidéo: Multiple Hypothesis Testing: Benjamini-Hochberg Procedure
- Vidéo: Big Data and Spurious Correlations
- Vidéo: Spurious Correlations: Stock Price Example
- Vidéo: How is Big Data Different?
- Vidéo: Bayesian vs. Frequentist
- Vidéo: Motivation for Bayesian Approaches
- Vidéo: Bayes' Theorem
- Vidéo: Applying Bayes' Theorem
- Vidéo: Naive Bayes: Spam Filtering
SEMAINE 2
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 vidéos, 1 lecture
- Vidéo: Simple Examples
- Vidéo: Structure of a Machine Learning Problem
- Vidéo: Classification with Simple Rules
- Vidéo: Learning Rules
- Vidéo: Rules: Sequential Covering
- Vidéo: Rules Recap
- Vidéo: From Rules to Trees
- Vidéo: Entropy
- Vidéo: Measuring Entropy
- Vidéo: Using Information Gain to Build Trees
- Vidéo: Building Trees: ID3 Algorithm
- Vidéo: Building Trees: C.45 Algorithm
- Vidéo: Rules and Trees Recap
- Vidéo: Overfitting
- Vidéo: Evaluation: Leave One Out Cross Validation
- Vidéo: Evaluation: Accuracy and ROC Curves
- Vidéo: Bootstrap Revisited
- Vidéo: Ensembles, Bagging, Boosting
- Vidéo: Boosting Walkthrough
- Vidéo: Random Forests
- Vidéo: Random Forests: Variable Importance
- Vidéo: Summary: Trees and Forests
- Vidéo: Nearest Neighbor
- Vidéo: Nearest Neighbor: Similarity Functions
- Vidéo: Nearest Neighbor: Curse of Dimensionality
- Reading: R Assignment: Classification of Ocean Microbes
Noté: R Assignment: Classification of Ocean Microbes
SEMAINE 3
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 vidéos
- Vidéo: Gradient Descent Visually
- Vidéo: Gradient Descent in Detail
- Vidéo: Gradient Descent: Questions to Consider
- Vidéo: Intuition for Logistic Regression
- Vidéo: Intuition for Support Vector Machines
- Vidéo: Support Vector Machine Example
- Vidéo: Intuition for Regularization
- Vidéo: Intuition for LASSO and Ridge Regression
- Vidéo: Stochastic and Batched Gradient Descent
- Vidéo: Parallelizing Gradient Descent
SEMAINE 4
Unsupervised Learning
A brief tour of selected unsupervised learning methods and an opportunity to apply techniques in practice on a real world problem.
4 vidéos
- Vidéo: K-means
- Vidéo: DBSCAN
- Vidéo: DBSCAN Variable Density and Parallel Algorithms
Noté: Kaggle Competition Peer Review
FAQ
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
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