Introduction to statistical learning

Transcription

1 Introduction to statistical learning 1. Introduction V. Lefieux June /42

2 Table of contents 2/42

3 Table of contents 3/42

4 Data everywhere 4/42

5 Data everywhere Before: structured data, generated by companies and organizations, regular but not so frequent updates (e.g monthly). Now: unstructured data, generated by users, real time data. 5/42

6 Some data generated by companies and organization 6/42

7 Some data generated by users 7/42

8 Some networks 8/42

9 And now health data 9/42

10 3 V? 10/42

11 4 V? 11/42

12 5 V? 12/42

13 The new oil? Clive Huby, /42

14 A landscape 14/42

15 Gartner hype cycle /42

16 Table of contents 16/42

17 A process: collecting, organizing (cleaning and storing), analyzing, visualizing large sets of data. An objective: discover useful information to improve business decisions. 17/42

18 A new idea? Four major influences act on data analysis today: The formal theories of statistics. Accelerating developments in computers and display devices. The challenge, in many fields, of more and ever larger bodies of data. The emphasis on quantification in an ever wider variety of disciplines. 18/42

19 Not so new! Data analysis and statistics: an expository overview J. W. Tukey and M. B. Wilk 1966 Four major influences act on data analysis today: The formal theories of statistics. Accelerating developments in computers and display devices. The challenge, in many fields, of more and ever larger bodies of data. The emphasis on quantification in an ever wider variety of disciplines. 19/42

20 Spam filter 20/42

21 Web search 21/42

22 Recommendations 22/42

23 Marketing 23/42

24 Customer relationship management (CRM) Hotel chain uses big data to increase bookings. Pizza chain earns more dough in bad weather. Music distributor applies big data for demand planning. Financial services company scores new clients. Retailer creates pregnancy detection model. 24/42

25 Smart grids And smart cities. 25/42

26 Genomics 26/42

27 Table of contents 27/42

28 The data scientist 28/42

29 Data scientist skills 29/42

30 Superhero skills? 30/42

31 Some definitions: is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics, similar to Knowledge Discovery in Databases (KDD). 31/42

32 Table of contents 32/42

33 Some definitions: Machine learning Machine learning is a field of computer science that often uses statistical techniques to give computers the ability to learn (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. 33/42

34 Some definitions: theory is a framework for machine learning drawing from the fields of statistics and functional analysis. theory deals with the problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, bioinformatics and baseball. 34/42

35 vs Machine learning Machine learning, from Artificial Intelligence: large scale applications, prediction accuracy., from Statistics: interpretability, precision, uncertainty, inference. For some statisticians: statistical learning is a mathematical formalisation of the machine learning. 35/42

36 Some concepts: online/offline learning Online learning (real-time): under time constraints. Some examples: Personalized advertising. Personalized healthcare. Navigation & transit tools. Autonomous cars. Load curve forecasts. Weather forecasts. Offline learning (batch). 36/42

37 Some concepts: supervised/unsupervised learning Supervised learning: Infer (predict) a function/relationship from labeled training data (e.g. classification, regression). Unsupervised learning: Find structure in unlabeled data (e.g. clustering). Even if it is more subjective than supervised learning, it can be useful as a pre-processing step for supervised learning. 37/42

38 Supervised learning There are many different paradigms, including: Parametric statistics (linear or non-linear). Non-parametric statistics (local estimation methods, e.g smoothing kernel methods, k-nearest neighbors). Tree based methods. Support Vector Machines. Deep learning. 38/42

39 Some key points Trade-off between prediction accuracy and interpretability. Avoid over-fitting. Parsimonious model vs (full) black box: less is more. 39/42

40 Table of contents 40/42

41 Outline Introduction. Unsupervised learning: PCA & clustering. Supervised learning: Cross validation & bootstrap. Reminders on linear regression & logistic regression. Tree based methods. Support Vector Machines. 41/42

42 Software tools 42/42