cs189 lecture 1

8/9/2019 cs189 lecture 1

http://slidepdf.com/reader/full/cs189-lecture-1 1/113

CS189/CS289AIntroduction to Machine Learning

Lecture 1: Overview

Alexei Efros and Peter Bartlett

January 20, 2015

1/37

http://find/

8/9/2019 cs189 lecture 1


Organizational Issues

2/37

http://find/

http://goback/

8/9/2019 cs189 lecture 1



Instructors: Peter Bartlett and Alyosha Efros.

2/37

http://find/

8/9/2019 cs189 lecture 1




GSIs: Peter Gao, Yun Park, Faraz Tavakoli, Kevin Tee, Pat Virtue,Christopher Xie, Daniel Xu, Yuchen Zhang.

2/37

http://find/

8/9/2019 cs189 lecture 1





Discussion sections: You choose. If the room is full, please go toanother one. (If necessary, we may offer some specialtysections—watch website for announcements.)

2/37

http://find/

http://goback/

8/9/2019 cs189 lecture 1






Office hours: see web site.

2/37

http://find/

8/9/2019 cs189 lecture 1






Office hours: see web site.

http://www-inst.eecs.berkeley.edu/∼cs189

bCourses (+ piazza, kaggle), office hours, syllabus, assignments,readings, lecture slides, announcements.

2/37

http://find/

8/9/2019 cs189 lecture 1



Assessment:CS189 Homework 40%

Implementation and application of methods. (Kaggle)

Mathematical/reinforcement of concepts.Seven total.Late policy: 5 slip days total. That’s it.

Midterm 20%(Thursday, March 19, in the lecture slot.)Final Exam 40%

3/37

http://find/

8/9/2019 cs189 lecture 1



Assessment:

CS289A Plus a project:

Homework 40%Midterm 20%Final Exam 20%Final Project 20%(due Friday, May 1. Proposal due Friday, April 3.)

4/37

http://find/

http://goback/

8/9/2019 cs189 lecture 1



(Real) Prerequisites:Math53 (vector calculus); Math54 (linear algebra); CS70 (discretemath, probability); CS188 (more probability, decision theory).

5/37

http://find/

8/9/2019 cs189 lecture 1




No screens in lectures. (To see why, google “laptops in class.”)

5/37

http://find/

http://goback/

8/9/2019 cs189 lecture 1





Ethics:

5/37

http://find/

8/9/2019 cs189 lecture 1





Ethics:Discussion of homework problems with other students is encouraged.

5/37

http://find/

8/9/2019 cs189 lecture 1





Ethics:Discussion of homework problems with other students is encouraged.All homeworks must be written individually (including programmingcomponents).

5/37

http://find/

8/9/2019 cs189 lecture 1





Ethics:Discussion of homework problems with other students is encouraged.All homeworks must be written individually (including programmingcomponents).Please read the department policy on academic dishonesty. We will be

actively checking for plagiarism.

5/37

http://find/

8/9/2019 cs189 lecture 1





Ethics:Discussion of homework problems with other students is encouraged.All homeworks must be written individually (including programmingcomponents).Please read the department policy on academic dishonesty. We will be

actively checking for plagiarism.Questions: Use piazza. Public and private.

5/37

T

http://find/

8/9/2019 cs189 lecture 1


Texts

6/37

CS189 I d i M hi L i

http://find/

8/9/2019 cs189 lecture 1


CS189: Introduction to Machine Learning

7/37

CS189 I d i M hi L i

http://find/

8/9/2019 cs189 lecture 1



Machine Learning

Systems that learn to solveinformation processing problems.

7/37

CS189 I t d ti t M hi L i

http://find/

8/9/2019 cs189 lecture 1



Machine Learning


LearnUse experience to improve performance:data, queries, interaction, experiments

Statistical issues are central.

7/37

CS189 I t d ti t M hi L i

http://find/

8/9/2019 cs189 lecture 1



Machine Learning


LearnUse experience to improve performance:data, queries, interaction, experiments

Statistical issues are central.

Systems

Computational issues are also central.

Algorithms, optimization.

7/37

An Overview of Machine Learning

http://find/

8/9/2019 cs189 lecture 1



1

2

3

8/37


http://find/

http://goback/

8/9/2019 cs189 lecture 1



1 Problems

2

3

8/37


http://find/

8/9/2019 cs189 lecture 1



1 Problems

2 Methods3

8/37


http://find/

8/9/2019 cs189 lecture 1



1 Problems

2 Methods3 Concepts

8/37


http://find/

8/9/2019 cs189 lecture 1



1 Problems

2 Methods3 Concepts

8/37

Classification Problems (Homework)

http://find/

8/9/2019 cs189 lecture 1



Email

ESL9/37


http://find/

8/9/2019 cs189 lecture 1



ESL

10/37

Classification

http://find/

8/9/2019 cs189 lecture 1


Classification

11/37

Classification

http://find/

8/9/2019 cs189 lecture 1


Classification

microsoft.com

12/37

Classification

http://find/

8/9/2019 cs189 lecture 1


Classification

apple.com

ESL13/37

Classification

http://find/

http://find/

8/9/2019 cs189 lecture 1


ISLR

14/37

Classification

http://find/

http://find/

8/9/2019 cs189 lecture 1


ISLR

15/37

Classification

http://find/

http://find/

8/9/2019 cs189 lecture 1


ESL16/37

Regression

http://find/

8/9/2019 cs189 lecture 1


g

ESL17/37

Regression

http://find/

8/9/2019 cs189 lecture 1


ESL18/37

Regression

http://find/

http://find/

8/9/2019 cs189 lecture 1


ESL

19/37

Regression

http://find/

http://find/

8/9/2019 cs189 lecture 1


ESL

20/37

Regression

http://find/

8/9/2019 cs189 lecture 1


ESL

21/37

Density Estimation

http://find/

8/9/2019 cs189 lecture 1


ESL

22/37

Density Estimation

http://find/

8/9/2019 cs189 lecture 1


ESL

23/37

Dimensionality Reduction

http://find/

8/9/2019 cs189 lecture 1


ESL

24/37


http://find/

http://find/

8/9/2019 cs189 lecture 1


ESL

25/37


http://find/

8/9/2019 cs189 lecture 1


ESL

26/37

Clustering

http://find/

http://find/

8/9/2019 cs189 lecture 1


ESL27/37

Clustering

http://find/

8/9/2019 cs189 lecture 1


28/37

Clustering

http://find/

8/9/2019 cs189 lecture 1


ESL

29/37

Clustering

http://find/

8/9/2019 cs189 lecture 1


ESL

30/37

Machine Learning Problems

http://find/

8/9/2019 cs189 lecture 1


Classification

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

Regression

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

Regression

Density estimation

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

Regression

Density estimation

Dimensionality reduction

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

Regression

Density estimation


Clustering

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

Regression

Density estimation


Clustering

Ranking

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

Regression

Density estimation


Clustering

Ranking

Collaborative filtering

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

Regression

Density estimation


Clustering

Ranking


Sequential decisionproblems:

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

Regression

Density estimation


Clustering

Ranking



bandits

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

Regression

Density estimation


Clustering

Ranking



banditscontextual bandits

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

RegressionDensity estimation


Clustering

Ranking



banditscontextual banditsdynamic pricing

31/37


http://find/

8/9/2019 cs189 lecture 1


Classification

RegressionDensity estimation


Clustering

Ranking



banditscontextual banditsdynamic pricingreinforcement learning

31/37


http://find/

8/9/2019 cs189 lecture 1


1 Problems

2 Methods

3 Concepts

32/37

Methods

http://find/

8/9/2019 cs189 lecture 1


Linear classifiers: Perceptron

Support vector machines

Gaussian class conditionals

Logistic regression

Naive Bayes

Linear discriminant analysisLinear regression

Decision trees, regression trees

Ensemble methods

Neural networksNearest neighbor

Principal components analysis

k-means clustering

33/37

Methods

http://find/

8/9/2019 cs189 lecture 1





Logistic regression

Naive Bayes



Ensemble methods



k-means clustering

1 Classification2 Regression

33/37

Methods

http://find/

8/9/2019 cs189 lecture 1





Logistic regression

Naive Bayes



Ensemble methods



k-means clustering

1 Probabilistic

modeling.

2 Prediction; not basedon a model.

33/37


http://goforward/

http://find/

http://goback/

8/9/2019 cs189 lecture 1


1 Problems

2 Methods

3 Concepts

34/37

Concepts

P di i b bili i d li

http://find/

8/9/2019 cs189 lecture 1


1 Prediction versus probabilistic modeling.

35/37

Concepts

P di i b bili i d li

http://find/

8/9/2019 cs189 lecture 1


1 Prediction versus probabilistic modeling.2 Probabilistic modeling:

35/37

Concepts

1 P di ti b bili ti d li

http://find/

http://goback/

8/9/2019 cs189 lecture 1



Generative versus discriminative models.

35/37

Concepts

1 Prediction ers s probabilistic modeling

http://find/

8/9/2019 cs189 lecture 1



Generative versus discriminative models.Maximum likelihood estimation.

35/37

Concepts

1 Prediction versus probabilistic modeling

http://find/

8/9/2019 cs189 lecture 1



Generative versus discriminative models.Maximum likelihood estimation.Bayesian inference.

35/37

Concepts


http://goforward/

http://find/

http://goback/

8/9/2019 cs189 lecture 1




3 Optimization.

35/37

Concepts


http://find/

8/9/2019 cs189 lecture 1




3 Optimization.

Convexity.

35/37

Concepts


http://find/

8/9/2019 cs189 lecture 1




3 Optimization.

Convexity.

(Stochastic) gradient methods.

35/37

Concepts


http://find/

8/9/2019 cs189 lecture 1


Prediction versus probabilistic modeling.2 Probabilistic modeling:


3 Optimization.

Convexity.

(Stochastic) gradient methods.Newton’s method.

35/37

Concepts


http://find/

8/9/2019 cs189 lecture 1




3 Optimization.

Convexity.


4 Controlling complexity:

35/37

Concepts


http://find/

8/9/2019 cs189 lecture 1




3 Optimization.

Convexity.



Bias-variance/approximation-estimation trade-off.

35/37

Concepts


http://find/

8/9/2019 cs189 lecture 1


p g2 Probabilistic modeling:


3 Optimization.

Convexity.



Bias-variance/approximation-estimation trade-off.Regularization

35/37

Concepts


http://find/

8/9/2019 cs189 lecture 1


p g2 Probabilistic modeling:


3 Optimization.

Convexity.




Priors

35/37

http://find/

8/9/2019 cs189 lecture 1


Concepts


8/9/2019 cs189 lecture 1


2 Probabilistic modeling:


3 Optimization.

Convexity.




Priors5 Practical issues:

Train/validate/test. Over-fitting.

35/37

Concepts


http://find/

8/9/2019 cs189 lecture 1


2 Probabilistic modeling:


3 Optimization.

Convexity.




Priors5 Practical issues:

Train/validate/test. Over-fitting.Resampling methods.

35/37

Overview (Part I: Bartlett)

Linear classification

http://find/

8/9/2019 cs189 lecture 1



36/37



http://find/

8/9/2019 cs189 lecture 1



Statistical learning background

36/37



http://find/

8/9/2019 cs189 lecture 1




Decision theory

36/37



http://find/

8/9/2019 cs189 lecture 1




Decision theoryGenerative and discriminative models

36/37



http://find/

8/9/2019 cs189 lecture 1




Decision theoryGenerative and discriminative modelsControlling complexity.

36/37



http://find/

8/9/2019 cs189 lecture 1




Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.

36/37



http://find/

8/9/2019 cs189 lecture 1



Decision theoryGenerative and discriminative modelsControlling complexity.Resampling, cross-validation.The multivariate normal distribution.

36/37



http://find/

8/9/2019 cs189 lecture 1




Linear regression

36/37



http://find/

8/9/2019 cs189 lecture 1




Linear regression

Optimization

36/37



http://find/

8/9/2019 cs189 lecture 1




Linear regression

Optimization

Linear Classification revisited

36/37



http://find/

8/9/2019 cs189 lecture 1




Linear regression

Optimization


Logistic regression

36/37



http://find/

8/9/2019 cs189 lecture 1




Linear regression

Optimization


Logistic regression

Linear Discriminant Analysis

36/37



http://find/

8/9/2019 cs189 lecture 1




Linear regression

Optimization


Logistic regression

Linear Discriminant AnalysisSupport vector machines

36/37



http://find/

8/9/2019 cs189 lecture 1




Linear regression

Optimization


Logistic regression

Linear Discriminant AnalysisSupport vector machines

Statistical learning theory

36/37

Overview (Part II: Efros)1 Memory-based/Instance-based learning

http://find/

8/9/2019 cs189 lecture 1


37/37


k-nearest-neighbor

http://find/

8/9/2019 cs189 lecture 1


37/37


k-nearest-neighborProperties of high dimensional spaces

http://find/

8/9/2019 cs189 lecture 1


Properties of high-dimensional spaces

37/37

http://find/

8/9/2019 cs189 lecture 1



k-nearest-neighborProperties of high-dimensional spaces

8/9/2019 cs189 lecture 1


Properties of high-dimensional spaces

distance learningEfficient indexing and retrieval methods

37/37

http://find/

8/9/2019 cs189 lecture 1




8/9/2019 cs189 lecture 1


Properties of high dimensional spaces


2 Decision treesClassification and regression trees

37/37



http://find/

8/9/2019 cs189 lecture 1


Properties of high dimensional spaces


2 Decision treesClassification and regression treesRandom Forests

37/37



http://find/

8/9/2019 cs189 lecture 1


p g p



3 Boosting

37/37



http://find/

8/9/2019 cs189 lecture 1


p g p



3 Boosting4 Neural networks / Deep Learning

37/37

http://find/

8/9/2019 cs189 lecture 1




8/9/2019 cs189 lecture 1





Multilayer perceptronsVariations such as convolutional nets

37/37



http://find/

8/9/2019 cs189 lecture 1





Multilayer perceptronsVariations such as convolutional netsExamples and applications

37/37



http://find/

8/9/2019 cs189 lecture 1





Multilayer perceptronsVariations such as convolutional netsExamples and applications

5

Unsupervised methods

37/37



http://find/

8/9/2019 cs189 lecture 1




3 Boosting4 Neural networks / Deep LearningMultilayer perceptronsVariations such as convolutional netsExamples and applications

5

Unsupervised methodsClustering

37/37



http://find/

8/9/2019 cs189 lecture 1




3

Boosting4 Neural networks / Deep LearningMultilayer perceptronsVariations such as convolutional netsExamples and applications

5

Unsupervised methodsClusteringDensity estimation

37/37



http://find/

8/9/2019 cs189 lecture 1


distance learningEfficient indexing and retrieval methods2 Decision trees

Classification and regression treesRandom Forests

3


5

Unsupervised methodsClusteringDensity estimationDimensionality reduction

37/37



http://find/

8/9/2019 cs189 lecture 1


distance learningEfficient indexing and retrieval methods2 Decision trees

Classification and regression treesRandom Forests

3


5

Unsupervised methodsClusteringDensity estimationDimensionality reductionApplications: Collaborative filtering, etc.

37/37

http://find/

Documents

cs189 lecture 1