MACHINE LEARNING APPROACHES USING MATLAB
Transcript of MACHINE LEARNING APPROACHES USING MATLAB
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PREMANANDA INDIC, PH.D.
DEPARTMENT OF ELECTRICAL ENGINEERING
MACHINE LEARNING APPROACHES USING MATLAB
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OUTLINEINTRODUCTION
DIFFERENT MACHINE LEARNING APPROACHES
DISCUSSION
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OUTLINEINTRODUCTION
DIFFERENT MACHINE LEARNING APPROACHES
DISCUSSION
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INTRODUCTIONWhat is Machine Learning ?
Machine Learning is a field of study that gives computers the ability to “learn” without being explicitly programmed
• Prediction
• Classification
Samuel AL, IBM J. Research & Development, 1959, vol. 3 (3), 210-229
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INTRODUCTIONToo many books spoil the curiosity
Start with Andrew Ng, Machine Learning, Stanford University available on YouTube
Some Statistics & Programming Knowledge Helps !
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INTRODUCTIONMachine Learning with MATLAB
https://commons.wikimedia.org/wiki/File:Man_Driving_Car_Cartoon_Vector.svg
http://clipart-library.com/mechanic-cliparts.html
Machine Learning Driving School
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INTRODUCTION
https://www.mathworks.com/academia/tah-portal/university-of-texas-at-tyler-1108545.html
Preprocessing Feature Extraction Feature SelectionData
Statistical or Machine Learning Models
30MinuteFeature Extraction
ORS Webinar Recordings https://www.uttyler.edu/research/ors-research-design-data-analysis-lab/ors-research-design-data-analysis-lab-resources/ors-consultant-recordings.php
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INTRODUCTION
Preprocessing Feature Extraction Feature SelectionData
Statistical or Machine Learning Models
30MinuteFeature Extraction
Workspace
Command window
Text Editor
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OUTLINEINTRODUCTION
DIFFERENT MACHINE LEARNING APPROACHES
DISCUSSION
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Statistical vs. Machine Learning ModelsPurpose:
Statistical models are used for inference (To find association between features and an outcome). Results should be interpretable.
Machine Learning models are used for prediction (Use features that can predict an outcome). Results may not be interpretable.
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Statistical vs. Machine Learning ModelsAssociation vs. Prediction
Philips Actiwatch 2
0 1 2 30
1000
2000
0 1 2 30
1000
2000
Healthy Individual
Individual with depression
N=24
𝑉𝐼 = 𝑚 × 𝑆𝐼 + 𝐶
𝑚 = 𝑟𝜎𝑉𝐼𝜎𝑆𝐼
𝐶 = 𝜇𝑉𝐼 −𝑚𝜇𝑆𝐼
෩𝑆𝐼 = 𝑎 × 𝑉𝐼 + 𝑏
Sensitivity & Specificity
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LEARNING APPROACHESSupervised Learning
Learning a relationship between features and the outcome using a training set
Unsupervised Learning
Learning underlying structures in features
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LEARNING APPROACHESSupervised Learning
• Linear Regression
• Logistic Regression
• Support Vector Machine
•Artificial Neural Network
• …………..
• ……………
• …………….
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LEARNING APPROACHESUnsupervised Learning
Clustering
• Principal Component Analysis
• Independent Component Analysis
• Singular Value Decomposition
• …….
• ………
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LEARNING APPROACHESDo machines actually “learn” ?
N=24
𝑉𝐼 = 𝑚 × 𝑆𝐼 + 𝐶
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LEARNING APPROACHESDo machines actually “learn” ?
𝐸 = σ𝑛=1𝑁 𝑒2
N=24
෪𝑉𝐼 = 𝑚 × 𝑆𝐼 + 𝐶
𝑒 𝑁 = 1 = ෪𝑉𝐼 𝑁 = 1 − 𝑉𝐼(𝑁 = 1)
𝑒 𝑁 = 2 = ෪𝑉𝐼 𝑁 = 2 − 𝑉𝐼 𝑁 = 2……………..
𝑒 𝑁 = 24 = ෪𝑉𝐼 𝑁 = 24 − 𝑉𝐼(𝑁 = 24)
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LEARNING APPROACHESDo machines actually “learn” ?
How do we find minimum E ?
N=24
෪𝑉𝐼 = 𝑚 × 𝑆𝐼 + 𝐶
0.1 0.6 0.8 0.01 0.5
1 10 0.01 0.001 0.002
8 7 0.0006 0.03 0.55
100 12 0.1 12 0.89
2 1 2 0.5 0.05
m
C
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LEARNING APPROACHESDo machines actually “learn” ?
How do we find minimum E ?
- Gradient Descent
by Louis Augustin Cauchy in 1847
N=24
෪𝑉𝐼 = 𝑚 × 𝑆𝐼 + 𝐶
෩𝑆𝐼 = 𝑎 × 𝑉𝐼 + 𝑏
Cm
E
Linear Regression
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LEARNING APPROACHESDo machines actually “learn” ?
Classification of High Risk (n=43) vs. Low Risk (n=95)
0 = Low Risk, 1 = High Risk
Logistic RegressionAccuracy ~73%
VIMeanVarianceSkewnessKurtosisPowerPeriod
1
0
𝑝 =1
1 + 𝑒−(𝑎 ×𝑉𝐼+𝑏 )
Linear Regression F
P=1
1+𝑒−(𝐴𝑇𝐹+𝐵 )
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LEARNING APPROACHESHow to implement in MATLAB ?
Step 1: Create an excel sheet with features
with class assignments
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LEARNING APPROACHESHow to implement in MATLAB ?
Step 2: Open MATLAB and drag the
excel file to workspace
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LEARNING APPROACHESHow to implement in MATLAB ?
Step 3: Click Import Selection and
import data
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LEARNING APPROACHESHow to implement in MATLAB ?
Step 4: Features are in workspace and ready
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LEARNING APPROACHESHow to implement in MATLAB ?
Step 5: Go to Apps,
-click classification learner,
- -select Logistic Regression
from Model Type
-click New Session,
-select from Workspace
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LEARNING APPROACHESHow to implement in MATLAB ?
Step 6: Set 10 fold Cross validation
- Start the session
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LEARNING APPROACHESNONLINEAR FEATURES
Carreiro, S, Chintha KK, Shrestha S, Chapman B, Smelson D, Indic P. Wearable sensor based detection of stress and craving in patients during treatment for substance use
disorder: A mixed methods pilot study. Drug and Alcohol Dependence. 2020, 107929
.
Cravings Detection
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LEARNING APPROACHES
Stress Detection Algorithm
Sloke Shrestha
2 : No Stress3: Stress
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LEARNING APPROACHESStress Detection Algorithm
2 : No Stress3: Stress
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SUMMARY
Preprocessing Feature Extraction Feature SelectionData
Statistical or Machine Learning Models
o Identification of Featureso Develop MATLAB code for feature extractiono Set up databaseo Assist with experiment protocol and data analysiso Machine Learning
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SUMMARY
Preprocessing Feature Extraction Feature SelectionData
Statistical or Machine Learning Models
o Ready to go features and Machine Learning ModelsCHIA
features
fast & efficient
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THANK YOU
Former Students:
Apurupa Amperayani (PhD Student, Arizona State University)
Jonathan Wells (PhD Student, UT Austin)
Pallavi Atluri Keerthi Chintha (Data Scientist, Wabtec Corporation)
Selorm Darkey (Business Intelligent Analyst, Taylor Solutions)
Current Students:
Sloke Shrestha, UG
Joshua Stapp, Graduate
Mohammed Alenazi, Graduate Pravitha Ramanand, PhD, Postdoc
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THANK YOU
Design of a wearable biosensor sensor system with wireless network for the remote detection of life threatening events in neonates.
National Science Foundation Smart & Connected
Health Grant
P. Indic (Lead PI, UT-Tyler)
D. Paydarfar (Co PI, UT-Austin)
H. Wang (Co PI, UMass Dartmouth)
Y. Kim (Co PI, UMass Dartmouth)
Pre-Vent
National Institute Of Health Grant
P. Indic (Analytical Core PI, UT-Tyler)
N. Ambal (PI, Univ. of Alabama, Birmingham)
Design of a wearable sensor system and associated algorithm to track suicidal ideation from movement variability and develop a novel objective marker of suicidal ideation and behavior risk in veterans. Clinical Science Research and Development Grant (approved for
funding),
P. Indic (site PI, UT-Tyler)
E.G. Smith (Project PI, VA)
P. Salvatore (Investigator, Harvard University)
Wearable system for the detection of addictionP. Indic (PI, UT-Tyler)
M. Reinhart (PI, ContinueYou, LLC
S. Carriero, (PI. Univ. of Mass. Med. School)
SBIR: RAE (Realize, Analyze, Engage) - A digital biomarker based detection and intervention system for stress and carvings during recovery from substance abuse disorders.PIs: M. Reinhardt, S. Carreiro, P. Indic
STARs Award
The University of Texas System P. Indic (PI, UT Tyler)
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DISCUSSION