Machine Learning in HEP and Multivariate Techniques
Prof. dr hab. Elżbieta Richter-Wąs
Machine Learning and Multivariate analyses in HEP
- Extracted from slides by H. Voss at SOS 2016 and K. Reygers lectures at Heilderbeg Univ.
What is Machine Learning
2
Multi-variate Classification
3
Classification: Different Approaches
4
Signal Probability Instead of Hard Decision
5
ROC Curve
6
Different Approaches to Classification
7
General Remarks and Multi-Variate Analyses
8
Classifiers and Their Properties
9
What are Multivariate Techniques?
10
Machine Learning - Multivariate Techniques
11
Event Classification
12
Regression
13
Regression -> model functional behaviour
14
HEP: Everything started Multivariate
15
Machine Learning in HEP
16
Machine Learning in HEP
17
Event Classification
18
Classification ↔ Regression
19
Event Classification
20
Receiver Operation Characteristic (ROC) curve
21
Receiver Operation Characteristic (ROC) curve
22
Event Classification -> finding the mapping function y(x)
23
Machine Learning Categories
24
Kernel Density Estimator
25
Kernel Density Estimator
26
K-Nearest Neighbour
27
Kernel Density Estimator
28
„Curse of Dimensionality”
29
Naive Bayesian Classifier (projective Likelihood Classifier)
30
De-Correlation
31
De-Correlation via PCA (Principal Component Analysis)
32
Decorrelation at work
33
Boosted Decision Trees
34
Boosting
35
Adaptive Boosting (AdaBoost)
36
Boosted Decision Trees
37
38 Some plots from
Basic terminology
39
40
Prediction: Least squares
41
Prediction: Least squares
42
Prediction: nearest neighbor classifier
43
Prediction: nearest neighbor classifier
44
Perfect classification?
45
Comparison
46
Bias-variance tradeoff
47
Where are the neural networks?
48
Neural networks
49
How do NNs work?
50
How do NNs learn?
51
How do NNs learn?
52
