DSU AI workshop

2023

University of Delaware

Department of Physics and Astronomy

federica bianco

Biden School of Public Policy and Administration

Data  Science Institute

https://github.com/fedhere/DSU2023_DAMADSCcientist/tree/main

https://slides.com/federicabianco/dsu23_1

Li et al. 2022

AILE: the first AI-based platform for the detection and study of Light Echoes

NSF Award #2108841

Pessimal AI problem:

• small training data
• inaccurate labels
• imbalance classes
• diverse morphology
• low SNR

Xiaolong Li

LSSTC Catalyst Fellow 2023

UDelaware->John Hopkins

AILE: the first AI-based platform for the detection and study of Light Echoes

YOLO3 + "attention" mechanism

precision 80% at 70% recall with a training set of 19 light echo examples! 

Xiaolong Li

LSSTC Catalyst Fellow 2023

UDelaware->John Hopkins

https://iopscience.iop.org/article/10.3847/1538-4357/aae7cf

Time ->

Language models for time-resolved image processing

Shar Daniels

UDel 1st year

ZTF time-resolved continuous readout images (w Igor Andreoni and Ashish Mahabal)

Transformer architecture

NN for language processing

who needs to learn

Educate Policy makers

without understanding how ML works policy makers do not have the instruments to regulate it

Education for the people

but does this put the burden on the victims?

Educating DS practitioners in communicating DS concepts

the put the burden back on the practitioners

Datascience Education to Help and Protect us

Jack Dorsey (Twitter CEO) at TED 2019

boring the TED audience with details

Zuckerberg (Facebook CEO) deflecting questions at senate hearing

Data Science is a black box

Models are neutral, data is biased

two dangerous data-ethics myths

used to:

• understand structure of feature space
• classify based on examples
• predict a continuous variable (regression)
  • understand which features are important in prediction (to get close to causality)

General ML concepts

Inferential AI

Generative AI

Generative AI

https://www.instagram.com/p/CtO_80PM6BD/

https://www.instagram.com/p/CtO_80PM6BD/

[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.

Arthur Samuel, 1959

what is a ML?

a model is a low dimensional representation of a higher dimensionality datase

what is a "model" in ML?

Any mathematical model with parameters that are

learned from the data

what is a ML "model"?

what is a ML "model"?

mathematical formula: y = ?         

mathematical formula: y = ax + b

what is a ML "model"?

mathematical formula: y = ax + b

what is a ML "model"?

what is machine learning?

ML: study, development, and applicaton of any model with parameters learnt from the data

time

time

time

which is the "best fit" line? A , B, C, D?

A

B

C

D

to select the best fit parameters we define a function of the parameters to minimize or maximize

Objective Function

Loss Function

x1

x2

to select the best fit parameters we define a function of the parameters to minimize or maximize

Objective Function

Loss Function

Objective Function

Loss Function

to select the best fit parameters we define a function of the parameters to minimize or maximize

Machine Learning models are parametrized representation of "reality"  where the parameters are learned from finite sets of realizations of that reality

(note: learning by instance, e.g. nearest neighbours, may not comply to this definition)

Machine Learning is the disciplines that conceptualizes, studies, and applies those models.

Key Concept

what is  machine learning?

model parameters are learned by calculating a loss function for diferent parameter sets and trying to minimize loss (or a target function and trying to maximize)

e.g.

L1  = |target - prediction|

Learning relies on the definition of a loss function

Machine Learning

Data driven models for exploration of structure

set up: All features known for all observations

Goal: explore structure in the data

- data compression

- understanding structure

Algorithms: Clustering, (...)

x

y

Unsupervised Learning

Data driven models for exploration of structure

Unsupervised Learning

Data driven models for prediction

set up: All features known for a sunbset of the data; one feature cannot be observed for the rest of the data

Goal: predicting missing feature

-  classification

- regression

Algorithms: regression, SVM, tree methods, k-nearest neighbors,            neural networks, (...)

x

y

Supervised Learning

Data driven models for prediction

set up: All features known for a sunbset of the data; one feature cannot be observed for the rest of the data

Goal: predicting missing feature

-  classification

- regression

Algorithms: regression, SVM, tree methods, k-nearest neighbors,            neural networks, (...)

x

y

Supervised Learning

Learning relies on the definition of a loss function

Machine Learning

Galileo Galilei 1610

Following: Djorgovski

https://events.asiaa.sinica.edu.tw/school/20170904/talk/djorgovski1.pdf

Experiment driven

what drives

inference

Enistein 1916

what drives

inference

Theory driven | Falsifiability

Experiment driven

Ulam 1947

Theory driven | Falsifiability

Experiment driven

Simulations | Probabilistic inference | Computation

http://www-star.st-and.ac.uk/~kw25/teaching/mcrt/MC_history_3.pdf

what drives

inference

what drives

astronomy

the 2000s

Theory driven | Falsifiability

Experiment driven

Simulations | Probabilistic inference | Computation

Big Data + Computation | pattern discovery | predict by association

data driven: lots of data, drop theory and use associations

algorithmic transparency

strictly policy issues:

proprietary algorithms + audability

https://www.americanscientist.org/article/a-peek-at-proprietary-algorithms

technical + policy issues:

data access and redress + data provenance

algorithmic transparency

trivially intuitive

generalized additive models

decision trees

SVM

Random Forest

Deep Learning

univaraite

linear

regression

algorithmic transparency

we're still trying to figure it out 

algorithmic transparency

trivially intuitive

generalized additive models

decision trees

SVM

Random Forest

Deep Learning

univaraite

linear

regression

algorithmic transparency

we're still trying to figure it out 

algorithmic transparency

trivially intuitive

generalized additive models

decision trees

Deep Learning

1

SVM

Random Forest

univaraite

linear

regression

algorithmic transparency

we're still trying to figure it out 

algorithmic transparency

trivially intuitive

univaraite

linear

regression

generalized additive models

decision trees

Deep Learning

SVM

Random Forest

time

algorithmic transparency

we're still trying to figure it out 

algorithmic transparency

1

Machine learning: any method that learns parameters from the data

2

The transparency of an algorithm is proportional to its complexity and the complexity of the data space

3

The transparency of an algorithm is limited by our own ability and preparedness to interpret it

Toward Interpretable Machine Learning, Samek+2003

algorithmic transparency

NN:

NN:

M-P Neuron McCulloch & Pitts 1943

M-P Neuron McCulloch & Pitts 1943

M-P Neuron McCulloch & Pitts 1943

M-P Neuron

M-P Neuron

its a classifier

M-P Neuron McCulloch & Pitts 1943

M-P Neuron

M-P Neuron McCulloch & Pitts 1943

M-P Neuron

if     is Bool (True/False)

what value of  corresponds to logical AND?

M-P Neuron McCulloch & Pitts 1943

The perceptron algorithm : 1958, Frank Rosenblatt

Perceptron

The perceptron algorithm : 1958, Frank Rosenblatt

.

.

.

output

weights

bias

linear regression:

Perceptron

Perceptrons are linear classifiers: makes its predictions based on a linear predictor function

combining a set of weights (=parameters) with the feature vector.

The perceptron algorithm : 1958, Frank Rosenblatt

x

y

Perceptrons are linear classifiers: makes its predictions based on a linear predictor function

combining a set of weights (=parameters) with the feature vector.

The perceptron algorithm : 1958, Frank Rosenblatt

x

y

Perceptrons are linear classifiers: makes its predictions based on a linear predictor function

combining a set of weights (=parameters) with the feature vector.

The perceptron algorithm : 1958, Frank Rosenblatt

x

y

1

0

{

{

.

.

.

output

activation function

weights

bias

perceptron

The perceptron algorithm : 1958, Frank Rosenblatt

Perceptrons are linear classifiers: makes its predictions based on a linear predictor function

combining a set of weights (=parameters) with the feature vector.

The perceptron algorithm : 1958, Frank Rosenblatt

output

activation function

weights

bias

sigmoid

.

.

.

Perceptrons are linear classifiers: makes its predictions based on a linear predictor function

combining a set of weights (=parameters) with the feature vector.

The perceptron algorithm : 1958, Frank Rosenblatt

output

activation function

weights

bias

.

.

.

Perceptron

The perceptron algorithm : 1958, Frank Rosenblatt

Perceptron

The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.

The embryo - the Weather Buerau's $2,000,000 "704" computer - learned to differentiate between left and right after 50 attempts in the Navy demonstration

NEW NAVY DEVICE LEARNS BY DOING; Psychologist Shows Embryo of Computer Designed to Read and Grow Wiser

July 8, 1958

The perceptron algorithm : 1958, Frank Rosenblatt

Perceptron

The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.

The embryo - the Weather Buerau's $2,000,000 "704" computer - learned to differentiate between left and right after 50 attempts in the Navy demonstration

NEW NAVY DEVICE LEARNS BY DOING; Psychologist Shows Embryo of Computer Designed to Read and Grow Wiser

July 8, 1958

x1

x2

b1

b2

b3

b

multilayer perceptron

w: weight

sets the sensitivity of a neuron

b: bias:

up-down weights a neuron

EXERCISE

output

how many parameters?

input layer

hidden layer

output layer

hidden layer

DNN:

multilayer perceptron

output

layer of perceptrons

multilayer perceptron

output

input layer

hidden layer

output layer

1970: multilayer perceptron architecture

Fully connected: all nodes go to all nodes of the next layer.

multilayer perceptron

output

layer of perceptrons

multilayer perceptron

output

layer of perceptrons

multilayer perceptron

layer of perceptrons

output

layer of perceptrons

multilayer perceptron

output

Fully connected: all nodes go to all nodes of the next layer.

layer of perceptrons

multilayer perceptron

output

Fully connected: all nodes go to all nodes of the next layer.

layer of perceptrons

w: weight

sets the sensitivity of a neuron

b: bias:

up-down weights a neuron

learned parameters

multilayer perceptron

output

Fully connected: all nodes go to all nodes of the next layer.

layer of perceptrons

w: weight

sets the sensitivity of a neuron

b: bias:

up-down weights a neuron

f: activation function:

turns neurons on-off

BINARY

CLASSIFICATION

input layer

hidden layer

output layer

P(0)

P(1)

input layer

hidden layer

output layer

P(C)

MULTICLASS

CLASSIFICATION

P(B)

P(A)

P(D)

input layer

hidden layer

output layer

REGRESSION

continuous value

variable

DNN:

EXERCISE

output

how many parameters?

input layer

hidden layer

output layer

hidden layer

EXERCISE

output

how many parameters?

input layer

hidden layer

output layer

hidden layer

EXERCISE

output

how many parameters?

input layer

hidden layer

output layer

hidden layer

EXERCISE

output

how many parameters?

input layer

hidden layer

output layer

hidden layer

35

DNN:

There are other things that change from model to model, but that are not decided based on the data, simply things we decide "a prior"

output

input layer

hidden layer

output layer

hidden layer

how many hyperparameters?

EXERCISE

GREEN: architecture hyperparameters

RED: training hyperparameters

output

input layer

hidden layer

output layer

hidden layer

1. number of layers-  1
2. number of neurons/layer-   
3. activation function/layer-  
4. layer connectivity-       
5. optimization metric - 1
6. optimization method - 1
7. parameters in optimization- M

how many hyperparameters?

EXERCISE

GREEN: architecture hyperparameters

RED: training hyperparameters

principle of parsimony

or Ockham's razor

Pluralitas non est ponenda sine neccesitate

William of Ockham (logician and Franciscan friar) 1300ca

but probably to be attributed to John Duns Scotus (1265–1308)

“Complexity needs not to be postulated without a need for it”

principle of parsimony

Peter Apian, Cosmographia, Antwerp, 1524 from Edward Grant,

"Celestial Orbs in the Latin Middle Ages", Isis, Vol. 78, No. 2. (Jun., 1987).

Geocentric models are intuitive:

from our perspective we see the Sun moving, while we stay still

the earth is round,