STAT 151B

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IntroductionStatistics 151B:

Modern Statistical Prediction andMachine Learning

Overview and introduction

STAT 151B

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Introduction

Administrative information

Homepage: http://www.stat.berkeley.edu/∼jon/

stat-151b-spring-2012

All announcements and materials here.No printouts distributed in lecture.

Instructor: Prof. J. McAuliffejon@stat.berkeley.eduoffice hours: Thu 12:30 PM - 1:30 PM

Graduate student instructor: Jeff Regierjeff@stat.berkeley.eduoffice hours: TBA

STAT 151B

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Introduction

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Course texts:T. Hastie, R. Tibshirani, J. Friedman, The Elementsof Statistical Learning: Data Mining, Inference andPrediction, 2nd ed., 5th printing. Free pdf.P. Dalgaard, Introductory Statistics with R, 2nd ed.

Prerequisites:basic linear algebra (Ax = b, Ax = λx)multivariate calculus (∂f/∂xi , ∇x f )statistical inference

This is not a math course, but it uses math in anessential way to make ideas precise.

STAT 151B

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Computing

Platform: the R statistical computing environment,http://www.R-project.org .Freely available, open source.Installation packages available for Windows, Mac,Linuxen.The de facto standard for statistical computing all overthe world.You will become proficient in R: section will cover theDalgaard book extensively (learning R and reviewingbasic statistical inference).

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Homework

Four assignments over the course of the semester, dueroughly every two weeks.Work in groups of three.Data analysis in R (submit a transcript of your Rsession, source files, brief written summaries), usingtechniques from the course.Problem-solving to check your understanding oftheories and methods.No late submissions.30% of the final grade.

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Midterm

Topics covered on the midterm will be clearly indicated.One of the weekly discussion sections will be devotedto review/Q&A.Thursday March 22nd, in class.30% of the final grade.

STAT 151B

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Final project

This year, something new: a competition on a contestdataset.Build the best prediction rule you can using ideas andmethods from the course.Write a project report describing your analysis andresults (guidelines provided beforehand).Work in the same group of three.Project grade depends on quality of work and report,not standing in the competition.I will open the contest on Kaggle in early April.Jeff and I will compete. . . prepare to get owned.40% of the final grade.

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Final project – alternative

A substantial data analysis project.You pick

an area/topic/field/subject you are interested in,a nontrivial dataset related to it,a question to answer or theory to test.

Address the question or theory using some of the ideasand methods from the course.Write a project report describing your analysis andresults (guidelines provided beforehand).Work in same group of three.40% of the final grade.

STAT 151B

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FINAL PROJECT DEADLINES

Thursday April 5th: for those opting out of competition,final project proposal due at the beginning of lecture.Friday May 11th: final project report due at 3pm.

Last day permitted by the university. No extensionis possible.

You cannot pass the course without doing the finalproject.Get in front of this project. Do not leave it to thelast minute.If you like, please come to my office hours with yourideas or questions about a candidate project.

STAT 151B

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Example: spam filtering

• Customize an email spam detection system.

STAT 151B

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Example: handwritten-digit recognition

Elements of Statistical Learning c!Hastie, Tibshirani & Friedman 2001 Chapter 1

Figure 1.2: Examples of handwritten digits from U.S.

postal envelopes.

STAT 151B

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Example: gene finding

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Example: typing cancer with microarrays

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STAT 151B

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Example: phoneme detection• Classify a recorded phoneme, based on a log-periodogram.

STAT 151B

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Example: Land usage from LANDSAT images• Classify the pixels in a LANDSAT image, according to usage:

{red soil, cotton, vegetation stubble, mixture, gray soil, damp gray soil,

very damp gray soil}

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Example: face detection

!"#$%&''( )*+,-./0%1-%2,./%#34./001-56%7/8/.814-% ((

9:;/31</-8,=%$/0>=806%?$4@=/A%/8%,=B%CDE

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Example: seizure prediction

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Ex: “Collaborative filtering” (the Netflix prize)

CS 277: Data Mining Netflix Competition Overview Padhraic Smyth, UC Irvine: 8

Ratings Data

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480,000 users

17,700 movies

STAT 151B

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Statistical prediction

AKA “supervised learning” in the machine learningcommunity. Ingredients of the general framework:An outcome Y (called the response variable,dependent variable, or target).A p-vector of observables X (called the predictors,covariates, features, inputs, regressors, or independentvariables).Regression: Y is a continuous quantity (price, weight,concentration).Classification: Y takes values in a finite, unordered set:

clinical outcome: { lived, died }consumer credit: { bankrupt in 2006, or not }cancer type: { breast, melanoma, leukemia, . . . }

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Objectives

We have training data {(x1, y1), . . . , (xN , yN)}. These areobservations, examples, or instances. (Don’t call themsamples; the whole training data set is one sample.)Using the training data, the goals are:

Build a rule to predict ynew from xnew.Understand which predictors are related to theresponse.Estimate what the quality of future predictions andinferences will be.

STAT 151B

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Schedule of topics

[See syllabus]

STAT 151B

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A statistician’s manifesto (Trevor Hastie)

Understand the ideas behind the statistical methods, soyou know how to use them, when to use them, whennot to use them.Complicated methods build on simple methods.Understand the simple methods first.The results of a method are of little use without anassessment of how well or poorly it is doing.

Corollary : simple methods are sometimes just asgood as complicated methods; “technology tendsto overwhelm common sense” (D. Freedman)

Statistical prediction is an active and exciting area ofresearch, touching science, industry, and finance.