Autonomous Intelligent Robotics Instructor: Shiqi Zhang - - PowerPoint PPT Presentation

Spring 2017 CIS 493, EEC 492, EEC 592:

Autonomous Intelligent Robotics

Instructor: Shiqi Zhang

http://eecs.csuohio.edu/~szhang/teaching/17spring/

Machine learning: an introduction

Slides adapted from Ray Mooney, Pedro Domingos, James Hays, and Yi-Fan Chang

A Few Quotes

• “A breakthrough in machine learning would be worth

ten Microsofts” (Bill Gates, Chairman, Microsoft)

• “Machine learning is the next Internet”

(Tony Tether, Director, DARPA)

• Machine learning is the hot new thing”

(John Hennessy, President, Stanford)

• “Web rankings today are mostly a matter of machine learning”

(Prabhakar Raghavan, Dir. Research, Yahoo)

• “Machine learning is going to result in a real revolution” (Greg

Papadopoulos, CTO, Sun)

• “Machine learning is today’s discontinuity”

(Jerry Yang, CEO, Yahoo)

So What Is Machine Learning?

• Automating automation
• Getting computers to program themselves
• Writing software is the bottleneck
• Let the data do the work instead!

Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program

Magic?

No, more like gardening

• Seeds = Algorithms
• Nutrients = Data
• Gardener = You
• Plants = Programs

7

Why Study Machine Learning? Engineering Better Computing Systems

• Develop systems that are too difficult/expensive to

construct manually because they require specific detailed skills or knowledge tuned to a specific task (knowledge engineering bottleneck).

• Develop systems that can automatically adapt and

customize themselves to individual users.

–

Personalized news or mail filter

–

Personalized tutoring

• Discover new knowledge from large databases (data

mining).

–

Market basket analysis (e.g. diapers and beer)

–

Medical text mining (e.g. migraines to calcium channel blockers to magnesium)

8

Why Study Machine Learning? The Time is Ripe

• Many basic effective and efficient

algorithms available.

• Large amounts of on-line data available.
• Large amounts of computational resources

available.

9

Related Disciplines

• Artificial Intelligence
• Data Mining
• Probability and Statistics
• Information theory
• Numerical optimization
• Computational complexity theory
• Control theory (adaptive)
• Psychology (developmental, cognitive)
• Neurobiology
• Linguistics
• Philosophy

10

Defining the Learning Task

Improve on task, T, with respect to

performance metric, P, based on experience, E.

T: Playing checkers P: Percentage of games won against an arbitrary opponent E: Playing practice games against itself T: Recognizing hand-written words P: Percentage of words correctly classified E: Database of human-labeled images of handwritten words T: Driving on four-lane highways using vision sensors P: Average distance traveled before a human-judged error E: A sequence of images and steering commands recorded while

• bserving a human driver.

T: Categorize email messages as spam or legitimate. P: Percentage of email messages correctly classified. E: Database of emails, some with human-given labels

ML in a Nutshell

• Tens of thousands of machine learning

algorithms

• Hundreds new every year
• Every machine learning algorithm has three

components:

– Representation – Evaluation – Optimization

Representation

• Decision trees
• Sets of rules / Logic programs
• Instances
• Graphical models (Bayes/Markov nets)
• Neural networks
• Support vector machines
• Model ensembles
• Etc.

Evaluation

• Accuracy
• Precision and recall
• Squared error
• Likelihood
• Posterior probability
• Cost / Utility
• Margin
• Entropy
• K-L divergence
• Etc.

Optimization

• Combinatorial optimization

– E.g.: Greedy search

• Convex optimization

– E.g.: Gradient descent

• Constrained optimization

– E.g.: Linear programming

Types of Learning

• Supervised (inductive) learning

– Training data includes desired outputs

• Unsupervised learning

– Training data does not include desired outputs

• Semi-supervised learning

– Training data includes a few desired outputs

• Reinforcement learning

– Rewards from sequence of actions

Inductive Learning

• Given examples of a function (X, F(X))
• Predict function F(X) for new examples X

– Discrete F(X): Classification – Continuous F(X): Regression – F(X) = Probability(X): Probability estimation

Supervised vs. Unsupervised

17 Supervised learning Unsupervised learning Semi-supervised learning

Supervised learning

Prediction

Supervised learning: Steps

Training Labels Training Images Training

Training

Image Features Image Features

Testing

Test Image Learned model Learned model

Slide credit: D. Hoiem and L. Lazebnik

Unsupervised learning

Unsupervised “Weakly” supervised Fully supervised Definition depends on task

Slide credit: L. Lazebnik

Generalization

• How well does a learned model generalize from the data it was trained on to

a new test set? Training set (labels known) Test set (labels unknown)

Slide credit: L. Lazebnik

Generalization

• Components of generalization error

–

Bias: how much the average model over all training sets differ from the true model?

• Error due to inaccurate assumptions/simplifications made by the

model

–

Variance: how much models estimated from different training sets differ from each other

• Underfitting: model is too “simple” to represent all the

relevant class characteristics

–

High bias and low variance

–

High training error and high test error

• Overfitting: model is too “complex” and fits irrelevant

characteristics (noise) in the data

–

Low bias and high variance

–

Low training error and high test error

Slide credit: L. Lazebnik

Overfitting Thriller! https://youtu.be/DQWI1kvmwRg

Generative vs. Discriminative Classifiers

Generative Models

• Represent both the data

and the labels

• Often, makes use of

conditional independence and priors

• Examples

–

Naïve Bayes classifier

–

Bayesian network

• Models of data may apply

to future prediction problems

Discriminatjve Models

• Learn to directly predict the

labels from the data

• Ofuen, assume a simple

boundary (e.g., linear)

• Examples

– Logistjc regression – SVM – Boosted decision trees

• Ofuen easier to predict a

label from the data than to model the data

Slide credit: D. Hoiem

N e a r e s t N e i g h b

• r

C l a s s i fj e r

• A

s s i g n l a b e l

• f

n e a r e s t t r a i n i n g d a t a p

• i

n t t

• e

a c h t e s t d a t a p

• i

n t

Voronoi partitioning of feature space for two-category 2D and 3D data

from Duda et al.

Source: D. Lowe

K-nearest neighbor

x x x x x x x x

• x2

x1 + +

1-nearest neighbor

x x x x x x x x

• x2

x1 + +

3-nearest neighbor

x x x x x x x x

• x2

x1 + +

5-nearest neighbor

x x x x x x x x

• x2

x1 + +

Using K-NN

• Simple, a good one to try fjrst
• With infjnite examples, 1-NN provably has error

that is at most twice Bayes optjmal error

Sample Applications

• Web search
• Computational biology
• Finance
• E-commerce
• Space exploration
• Robotics (reasoning, planning, control, etc)
• Information extraction
• Social networks
• Debugging
• [Your favorite area]