SCUOLA DI SPECIALIZZAZIONE IN FISICA MEDICA. Sistemi di Elaborazione dell Informazione. Introduzione. Ruggero Donida Labati

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1 SCUOLA DI SPECIALIZZAZIONE IN FISICA MEDICA Sistemi di Elaborazione dell Informazione Introduzione Ruggero Donida Labati Dipartimento di Informatica via Bramante 65, Crema (CR), Italy

2 Outline del corso Introduzione all intelligenza computazionale. Analisi continua dei dati: regressione (regressione polinomiale, regressione lineare Bayesiana, reti neurali artificiali). Analisi discreta dei dati: classificazione (metodi di apprendimento supervisionato, k-nearest neighbors, reti neurali artificiali), clusterizzazione (metodi di apprendimento non supervisionato, reti neurali artificiali). Riduzione della dimensionalità (principal component analysis, independent component analysis, metodi di selezione delle caratteristiche). Applicazioni in ambiente Matlab.

3 Modalità d esame Discussione di un articolo scientifico che utilizza tecniche di intelligenza computazionale nell ambito della fisica medica. L articolo scientifico deve essere concordato con il docente. Progetto da concordare con il docente.

4 Sistemi di elaborazione dell informazione

5 Esempi applicativi (1/3) Will respond to treatment Patient Blood sample Mass spectrometry Protormics profile Model Will not respond to treatment Cannot make decision Patient Blood sample Cell image Model Leukemia Helty patient

6 Esempi applicativi (2/3) Model Will survive for 3.5 year Patient Biopsy Gene expression profile Patient Biomarkers, clinical and demographic data Model Otpimal dosage is X/week

7 Esempi applicativi (3/3)

8 Introduzione all intelligenza computazionale

9 Computational intelligence Two Big Questions: How does a human mind work, and Can non-humans display an intelligent behavior? These questions are still unanswered (Essential English Dictionary, Collins, London, 2008) Intelligence is the ability to understand and learn things. 2 Intelligence is the ability to think and understand instead of doing things by instinct or automatically. Summarizing, intelligence: ability to learn and understand, to solve problems and to make decisions

10 Goal of computational intelligence The goal of computational intelligence (CI) is to make machines do things that would require intelligence if done by humans. One of the most significant papers on machine intelligence: Computing Machinery and Intelligence, written by Alan Turing Is there thought without experience? Is there mind without communication? Is there language without living? Is there intelligence without life? That is: Can machines think?

Turing Imitation Game (1/3) First phase:

11 Turing Imitation Game (1/3) First phase: The interrogator, a man and a woman are each placed in separate rooms. The interrogator has to discover who is the man and who is the woman by questioning them. The man should deceive the interrogator that he is the woman, while the woman has to convince the interrogator that she is the woman.

If the computer can fool the interrogator as often as the man did,

12 Turing Imitation Game (2/3) Second phase: The man is replaced by a computer programmed to deceive the interrogator as the man did. If the computer can fool the interrogator as often as the man did, we may say this computer has passed the intelligent behaviour test.

13 Turing Imitation Game (3/3) The Turing test: By maintaining communication between the human and the machine via terminals, gives us an objective standard view on intelligence. The test itself is quite independent from the details of the experiment It can be conducted as a two- phase game, or even as a single-phase game (the interrogator needs to choose between the human and the machine) Turing believed that by the end of the 20th century it would be possible to program a digital computer to play the imitation game It took a bit more: on June 2014, a chatbox of the University of Reading (UK) fooled 33% of the judges into thinking it was Eugene Goostman, a 13-year-old Ukrainian boy

14 Regression Model Will survive for 3.5 year Patient Biopsy Gene expression profile Patient Biomarkers, clinical and demographic data Model Otpimal dosage is X/week

15 Regression examples Supervised learning

16 Regression: some approaches Polynomial curve fitting Statistical regression Feedforward neural networks Radial basis functions neural networks

17 Regression: polynomial curve fitting

18 Regression: feedforward neural networks

19 Classification Identifying to which of a set of categories a new observation belongs, on the basis of a training set of data Supervised learning

20 Classification examples Some our works Acute Limphoblastic Leucemia "healty cell" & "lymphoblast Wildfires "Smoke frame" & "not smoke frame" Wood 21 classes

21 Classification: pattern recognition

22 y Classification: 2D example Output space A=0 B=1 C=2 D= x

23 Classification: some approaches Linear classifier Quadratic classifier K-nearest neighbors Feedforward neural networks Support vector machines Deep learning

24 Deep learning

25 Dimensionality reduction The process of reducing the number of random variables under consideration, via obtaining a set of principal variables Feature selection and feature extraction Supervised or unsupervided

26 Dimensionality reduction: examples

27 Dimensionality reduction: some approaches Principal component analysis Linear discriminant analysis Indipendent component analysis Forward feature selection Backward feature selection

28 Clustering Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).

29 Clustering: example

30 Clustering: some approaches K-means Self organized maps Fuzzy c-means

31 Other CI techniques: fuzzy logic (1/2) Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1. Fuzzy logic has been employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when linguistic variables are used, these degrees may be managed by specific (membership) functions.

32 Other CI techniques: fuzzy logic (2/2)

33 Other CI techniques: evolutionary algorithms

34 Suggested lectures I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016, Book in preparation for MIT Press, M.l Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems,2nd ed., Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Inc., New York, NY, USA, 1995.

35 Regression The following slides are from: C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Inc., New York, NY, USA, 1995

36 Polynomial Curve Fitting

37 Sum-of-Squares Error Function

38 0 th Order Polynomial

39 1 st Order Polynomial

40 3 rd Order Polynomial

41 9 th Order Polynomial

42 Over-fitting Root-Mean-Square (RMS) Error:

43 Polynomial Coefficients

44 9 th Order Polynomial Data Set Size:

45 9 th Order Polynomial Data Set Size:

46 Regularization Penalize large coefficient values

47 Regularization:

48 Regularization:

49 Regularization: vs.

50 Polynomial Coefficients

51 Apples and Oranges Probability Theory

52 Probability Theory Marginal Probability Joint Probability Conditional Probability

53 Probability Theory Sum Rule Product Rule

54 The Rules of Probability Sum Rule Product Rule

55 Bayes Theorem posterior likelihood prior

56 The example p(b = r) = 4/10 p(b = b) = 6/10 p(f = a B = r) = 1/4 p(f = o B = r) = 3/4 p(f = a B = b) = 3/4 p(f = o B = b) = 1/4

57 Probability Densities

58 Expectations Conditional Expectation (discrete) Approximate Expectation (discrete and continuous)

59 Variances and Covariances

60 The Gaussian Distribution

61 Gaussian Mean and Variance

62 The Multivariate Gaussian

63 Gaussian Parameter Estimation Likelihood function

64 Maximum (Log) Likelihood

65 Maximum Likelihood Determine by minimizing sum-of-squares error,.

66 Curve Fitting Re-visited

67 Predictive Distribution

68 Bayesian Curve Fitting

69 Bayesian Predictive Distribution

70 Introduction to Matlab

Introduction to Machine Learning. Katherine Heller Deep Learning Summer School 2018

Introduction to Machine Learning Katherine Heller Deep Learning Summer School 2018 Outline Kinds of machine learning Linear regression Regularization Bayesian methods Logistic Regression Why we do this