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Organizing Secretary
Dr.R.Gunavathi,
Head & Associate professor,
PG & Research Department of Computer
Applications
PG & Research Department of Computer Applications
organized
IoT – Applications and Machine Learning
-By Sushama
Assistant Professor (CSE)
JECRC University
“Learning is any process by which a system
improves performance from experience.”
- Herbert Simon
Definition by Tom Mitchell (1998):
Machine Learning is the study of algorithms that
• improve their performance P
• at some task T
• with experience E.
A well-defined learning task is given by <P, T,
E>.
3
Machine
Learning
Computer Output
Computer
Data
Output
Program
4
Data
Program
ML is used when:
• Human expertise does not exist (navigating on Mars)
• Humans can’t explain their expertise (speech
recognition)
• Models must be customized (personalized medicine)
• Models are based on huge amounts of data
(genomics)
Learning isn’t always useful:
• There is no need to “learn” to calculate payroll
5
6
Slide credit: Geoffrey
7
• Recognizing patterns:
– Facial identities or facial expressions
– Handwritten or spoken words
– Medical images
• Generating patterns:
– Generating images or motion sequences
• Recognizing anomalies:
– Unusual credit card transactions
– Unusual patterns of sensor readings in a nuclear
power plant
• Prediction:
– Future stock prices or currency exchange rates
8
• Web search
• Computational
biology
• Finance
• E-commerce
• Space exploration
• Robotics
• Information
extraction
• Social networks
• Debugging software
• [Your favorite area]
“Machine Learning: Field of study that gives
computers the ability to learn without being
explicitly programmed.” -Arthur Samuel
(1959)
9
10
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
observing 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
11
• Nevada made it legal for
autonomous cars to drive
on roads in June 2011
• As of 2013, four states
(Nevada, Florida, California,
and Michigan) have
legalized autonomous cars
Penn’s Autonomous Car 
(Ben Franklin Racing Team)
12
13
Laser Terrain
Mapping
Stanley
Learning from Human
Drivers
Sebastian
Adaptive
Vision
Pat
h
Plannin
g
Images and movies taken from Sebastian Thrun’smultimedia
w1e4bsite.
15
pixels
edges
object parts
(combination
of edges)
object models
16
17
 Trained on 4 classes
(cars, faces,
motorbikes, airplanes).
 Second layer: Shared-
features and object-
specific features.
 Third layer: More
specific features.
18
[Farabet et al. ICML 2012, PAMI
2013]
19
Input
images
Samples
from
feedforward
Inference
(control)
Samples
from Full
posterior
inference
Generating posterior samples from faces by “filling in”experiments
(cf. Lee and Mumford, 2003). Combine bottom-up and top-down
inference.
Slide credit: Andrew
20
A Typical Speech Recognition
System
ML used to predict of phone states from the sound
spectrogram
Deep learning has state-of-the-art
results
# Hidden Layers 1 2 4 8 10 12
Word Error Rate % 16.0 12.8 11.4 10.9 11.0 11.1
Baseline GMM performance = 15.4%
[Zeiler et al. “On rectified linear units for
speech recognition” ICASSP 2013]
21
Slide credit: Li Deng, MS
22
23
• Supervised (inductive) learning
– Given: training data + desired outputs (labels)
• Unsupervised learning
– Given: training data (without desired outputs)
• Semi-supervised learning
– Given: training data + a few desired outputs
• Reinforcement learning
– Rewards from sequence of actions
24
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f(x) to predict y givenx
– y is real-valued == regression
9
8
7
6
5
4
3
2
1
0
1970 1980 1990 2000 2010 2020
SeptemberArcticSeaIce
Extent(1,000,000sq
km)
26
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f(x) to predict y givenx
– y is categorical == classification
Breast Cancer (Malignant / Benign)
1(Malignant)
0(Benign)
Tumor Size
27
28
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f(x) to predict y givenx
– y is categorical == classification
Breast Cancer (Malignant / Benign)
1(Malignant)
0(Benign)
Tumor Size
Tumor Size
29
• Given (x1, y1), (x2, y2), ..., (xn, yn)
• Learn a function f(x) to predict y given
x
– y is categorical == classification
Breast Cancer (Malignant / Benign)
1(Malignant)
0(Benign)
Tumor Size
Predict Benign Predict Malignant
Tumor Size
Tumor
Size
Ag
e
- Clump Thickness
- Uniformity of Cell Size
- Uniformity of Cell
Shape
…
• x can be multi-dimensional
– Each dimension corresponds to an
attribute
30
• Given x1, x2, ..., xn (without labels)
• Output hidden structure behind the x’s
– E.g., clustering
31
Genes
Individuals
Genomics application: group individuals by genetic
similarity
32
Organize computing clusters Social network analysis
Image credit: NASA/JPL-Caltech/E. Churchwell (Univ. of Wisconsin, Madison)
Astronomical data analysisMarket segmentation
33
34
• Independent component analysis – separate a
combined signal into its original sources
35
• Independent component analysis – separate a
combined signal into its original sources
36
• Given a sequence of states and actions
with (delayed) rewards, output a policy
– Policy is a mapping from states  actions
that tells you what to do in a given state
• Examples:
– Credit assignment problem
– Game playing
– Robot in a maze
– Balance a pole on your hand
37
Agent and environment interact at discrete timesteps
Agent observes state at step t: st S
: t  0, 1, 2, K
produces action at step t : at  A(st )
gets resulting reward :
and resulting next state :
rt1 
st 1
. . . st at
rt +1 st +1
at +1
rt +2 st+2
at +2
rt +3 st +3
. . .
at +3
38
39
• Learn policy from user demonstrations
40
• Choose the training experience
• Choose exactly what is to be learned
– i.e. the target function
• Choose how to represent the target
function
• Choose a learning algorithm to infer the
target function from the experience
Environment/
Experience
Learner
Knowledge
Performance
Element
Training
data
Testing
data
41
• We generally assume that the training and test
examples are independently drawn from the
same overall distribution of data
– We call this “i.i.d” which stands for “independent and
identically distributed”
• If examples are not independent, requires
collective classification
• If test distribution is different, requires
transfer learning
42
• Tens of thousands of machine
learning algorithms
– Hundreds new every year
• Every ML algorithm has three
components:
– Representation
– Optimization
– Evaluation
43
44
• Numerical functions
– Linear regression
– Neural networks
– Support vector machines
• Symbolic functions
– Decision trees
– Rules in propositional logic
– Rules in first-order predicate logic
• Instance-based functions
– Nearest-neighbor
– Case-based
• Probabilistic Graphical Models
– Naïve Bayes
– Bayesian networks
– Hidden-Markov Models (HMMs)
– Probabilistic Context Free Grammars
(PCFGs)
– Markov networks
45
• Gradient descent
– Perceptron
– Backpropagation
• Dynamic Programming
– HMM Learning
– PCFG Learning
• Divide and Conquer
– Decision tree induction
– Rule learning
• Evolutionary Computation
– Genetic Algorithms (GAs)
– Genetic Programming (GP)
– Neuro-evolution
47
• Accuracy
• Precision and recall
• Squared error
• Likelihood
• Posterior probability
• Cost / Utility
• Margin
• Entropy
• K-L divergence etc.
• Understand domain, prior knowledge, and goals
• Data integration, selection, cleaning, pre-
processing, etc.
• Learn models
• Interpret results
• Consolidate and deploy discovered knowledge
Loop
48
49
• Learning can be viewed as using direct or
indirect experience to approximate a chosen
target function.
• Function approximation can be viewed as a
search through a space of hypotheses
(representations of functions) for one that best
fits a set of training data.
• Different learning methods assume different
hypothesis spaces (representation languages)
and/or employ different search techniques.
50
51
• 1950s
– Samuel’s checker player
– Selfridge’s Pandemonium
• 1960s:
– Neural networks: Perceptron
– Pattern recognition
– Learning in the limit theory
– Minsky and Papert prove limitations of Perceptron
• 1970s:
– Symbolic concept induction
– Winston’s arch learner
– Expert systems and the knowledge acquisition
bottleneck
– Quinlan’s ID3
– Michalski’s AQ and soybean diagnosis
– Scientific discovery with BACON
– Mathematical discovery with AM
52
• 1980s:
– Advanced decision tree and rule learning
– Explanation-based Learning (EBL)
– Learning and planning and problem solving
– Utility problem
– Analogy
– Cognitive architectures
– Resurgence of neural networks (connectionism,
backpropagation)
– Valiant’s PAC Learning Theory
– Focus on experimental methodology
• 1990s
– Data mining
– Adaptive software agents and web applications
– Text learning
– Reinforcement learning (RL)
– Inductive Logic Programming (ILP)
– Ensembles: Bagging, Boosting, and Stacking
– Bayes Net learning
53
• 2000s
– Support vector machines & kernel methods
– Graphical models
– Statistical relational learning
– Transfer learning
– Sequence labeling
– Collective classification and structured outputs
– Computer Systems Applications (Compilers, Debugging, Graphics, Security)
– E-mail management
– Personalized assistants that learn
– Learning in robotics and vision
• 2010s
– Deep learning systems
– Learning for big data
– Bayesian methods
– Multi-task & lifelong learning
– Applications to vision, speech, social networks, learning to read, etc.
– ???
54
• Supervised learning
– Decision tree induction
– Linear regression
– Logistic regression
– Support vector
machines & kernel
methods
– Model ensembles
– Bayesian learning
– Neural networks &
deep learning
– Learning theory
• Unsupervised
learning
– Clustering
– Dimensionality
reduction
• Reinforcement
learning
– Temporal
difference
learning
– Q learning
• Evaluation
• Applications
IoT and Machine Learning Applications
IoT and Machine Learning Applications

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IoT and Machine Learning Applications

  • 1. Organizing Secretary Dr.R.Gunavathi, Head & Associate professor, PG & Research Department of Computer Applications PG & Research Department of Computer Applications organized IoT – Applications and Machine Learning
  • 2. -By Sushama Assistant Professor (CSE) JECRC University
  • 3. “Learning is any process by which a system improves performance from experience.” - Herbert Simon Definition by Tom Mitchell (1998): Machine Learning is the study of algorithms that • improve their performance P • at some task T • with experience E. A well-defined learning task is given by <P, T, E>. 3
  • 5. ML is used when: • Human expertise does not exist (navigating on Mars) • Humans can’t explain their expertise (speech recognition) • Models must be customized (personalized medicine) • Models are based on huge amounts of data (genomics) Learning isn’t always useful: • There is no need to “learn” to calculate payroll 5
  • 7. 7 • Recognizing patterns: – Facial identities or facial expressions – Handwritten or spoken words – Medical images • Generating patterns: – Generating images or motion sequences • Recognizing anomalies: – Unusual credit card transactions – Unusual patterns of sensor readings in a nuclear power plant • Prediction: – Future stock prices or currency exchange rates
  • 8. 8 • Web search • Computational biology • Finance • E-commerce • Space exploration • Robotics • Information extraction • Social networks • Debugging software • [Your favorite area]
  • 9. “Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.” -Arthur Samuel (1959) 9
  • 10. 10 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 observing 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
  • 11. 11
  • 12. • Nevada made it legal for autonomous cars to drive on roads in June 2011 • As of 2013, four states (Nevada, Florida, California, and Michigan) have legalized autonomous cars Penn’s Autonomous Car  (Ben Franklin Racing Team) 12
  • 13. 13
  • 14. Laser Terrain Mapping Stanley Learning from Human Drivers Sebastian Adaptive Vision Pat h Plannin g Images and movies taken from Sebastian Thrun’smultimedia w1e4bsite.
  • 15. 15
  • 17. 17
  • 18.  Trained on 4 classes (cars, faces, motorbikes, airplanes).  Second layer: Shared- features and object- specific features.  Third layer: More specific features. 18
  • 19. [Farabet et al. ICML 2012, PAMI 2013] 19
  • 20. Input images Samples from feedforward Inference (control) Samples from Full posterior inference Generating posterior samples from faces by “filling in”experiments (cf. Lee and Mumford, 2003). Combine bottom-up and top-down inference. Slide credit: Andrew 20
  • 21. A Typical Speech Recognition System ML used to predict of phone states from the sound spectrogram Deep learning has state-of-the-art results # Hidden Layers 1 2 4 8 10 12 Word Error Rate % 16.0 12.8 11.4 10.9 11.0 11.1 Baseline GMM performance = 15.4% [Zeiler et al. “On rectified linear units for speech recognition” ICASSP 2013] 21
  • 22. Slide credit: Li Deng, MS 22
  • 23. 23
  • 24. • Supervised (inductive) learning – Given: training data + desired outputs (labels) • Unsupervised learning – Given: training data (without desired outputs) • Semi-supervised learning – Given: training data + a few desired outputs • Reinforcement learning – Rewards from sequence of actions 24
  • 25. • Given (x1, y1), (x2, y2), ..., (xn, yn) • Learn a function f(x) to predict y givenx – y is real-valued == regression 9 8 7 6 5 4 3 2 1 0 1970 1980 1990 2000 2010 2020 SeptemberArcticSeaIce Extent(1,000,000sq km) 26
  • 26. • Given (x1, y1), (x2, y2), ..., (xn, yn) • Learn a function f(x) to predict y givenx – y is categorical == classification Breast Cancer (Malignant / Benign) 1(Malignant) 0(Benign) Tumor Size 27
  • 27. 28 • Given (x1, y1), (x2, y2), ..., (xn, yn) • Learn a function f(x) to predict y givenx – y is categorical == classification Breast Cancer (Malignant / Benign) 1(Malignant) 0(Benign) Tumor Size Tumor Size
  • 28. 29 • Given (x1, y1), (x2, y2), ..., (xn, yn) • Learn a function f(x) to predict y given x – y is categorical == classification Breast Cancer (Malignant / Benign) 1(Malignant) 0(Benign) Tumor Size Predict Benign Predict Malignant Tumor Size
  • 29. Tumor Size Ag e - Clump Thickness - Uniformity of Cell Size - Uniformity of Cell Shape … • x can be multi-dimensional – Each dimension corresponds to an attribute 30
  • 30. • Given x1, x2, ..., xn (without labels) • Output hidden structure behind the x’s – E.g., clustering 31
  • 31. Genes Individuals Genomics application: group individuals by genetic similarity 32
  • 32. Organize computing clusters Social network analysis Image credit: NASA/JPL-Caltech/E. Churchwell (Univ. of Wisconsin, Madison) Astronomical data analysisMarket segmentation 33
  • 33. 34 • Independent component analysis – separate a combined signal into its original sources
  • 34. 35 • Independent component analysis – separate a combined signal into its original sources
  • 35. 36 • Given a sequence of states and actions with (delayed) rewards, output a policy – Policy is a mapping from states  actions that tells you what to do in a given state • Examples: – Credit assignment problem – Game playing – Robot in a maze – Balance a pole on your hand
  • 36. 37 Agent and environment interact at discrete timesteps Agent observes state at step t: st S : t  0, 1, 2, K produces action at step t : at  A(st ) gets resulting reward : and resulting next state : rt1  st 1 . . . st at rt +1 st +1 at +1 rt +2 st+2 at +2 rt +3 st +3 . . . at +3
  • 37. 38
  • 38. 39 • Learn policy from user demonstrations
  • 39. 40
  • 40. • Choose the training experience • Choose exactly what is to be learned – i.e. the target function • Choose how to represent the target function • Choose a learning algorithm to infer the target function from the experience Environment/ Experience Learner Knowledge Performance Element Training data Testing data 41
  • 41. • We generally assume that the training and test examples are independently drawn from the same overall distribution of data – We call this “i.i.d” which stands for “independent and identically distributed” • If examples are not independent, requires collective classification • If test distribution is different, requires transfer learning 42
  • 42. • Tens of thousands of machine learning algorithms – Hundreds new every year • Every ML algorithm has three components: – Representation – Optimization – Evaluation 43
  • 43. 44 • Numerical functions – Linear regression – Neural networks – Support vector machines • Symbolic functions – Decision trees – Rules in propositional logic – Rules in first-order predicate logic • Instance-based functions – Nearest-neighbor – Case-based • Probabilistic Graphical Models – Naïve Bayes – Bayesian networks – Hidden-Markov Models (HMMs) – Probabilistic Context Free Grammars (PCFGs) – Markov networks
  • 44. 45 • Gradient descent – Perceptron – Backpropagation • Dynamic Programming – HMM Learning – PCFG Learning • Divide and Conquer – Decision tree induction – Rule learning • Evolutionary Computation – Genetic Algorithms (GAs) – Genetic Programming (GP) – Neuro-evolution
  • 45. 47 • Accuracy • Precision and recall • Squared error • Likelihood • Posterior probability • Cost / Utility • Margin • Entropy • K-L divergence etc.
  • 46. • Understand domain, prior knowledge, and goals • Data integration, selection, cleaning, pre- processing, etc. • Learn models • Interpret results • Consolidate and deploy discovered knowledge Loop 48
  • 47. 49 • Learning can be viewed as using direct or indirect experience to approximate a chosen target function. • Function approximation can be viewed as a search through a space of hypotheses (representations of functions) for one that best fits a set of training data. • Different learning methods assume different hypothesis spaces (representation languages) and/or employ different search techniques.
  • 48. 50
  • 49. 51 • 1950s – Samuel’s checker player – Selfridge’s Pandemonium • 1960s: – Neural networks: Perceptron – Pattern recognition – Learning in the limit theory – Minsky and Papert prove limitations of Perceptron • 1970s: – Symbolic concept induction – Winston’s arch learner – Expert systems and the knowledge acquisition bottleneck – Quinlan’s ID3 – Michalski’s AQ and soybean diagnosis – Scientific discovery with BACON – Mathematical discovery with AM
  • 50. 52 • 1980s: – Advanced decision tree and rule learning – Explanation-based Learning (EBL) – Learning and planning and problem solving – Utility problem – Analogy – Cognitive architectures – Resurgence of neural networks (connectionism, backpropagation) – Valiant’s PAC Learning Theory – Focus on experimental methodology • 1990s – Data mining – Adaptive software agents and web applications – Text learning – Reinforcement learning (RL) – Inductive Logic Programming (ILP) – Ensembles: Bagging, Boosting, and Stacking – Bayes Net learning
  • 51. 53 • 2000s – Support vector machines & kernel methods – Graphical models – Statistical relational learning – Transfer learning – Sequence labeling – Collective classification and structured outputs – Computer Systems Applications (Compilers, Debugging, Graphics, Security) – E-mail management – Personalized assistants that learn – Learning in robotics and vision • 2010s – Deep learning systems – Learning for big data – Bayesian methods – Multi-task & lifelong learning – Applications to vision, speech, social networks, learning to read, etc. – ???
  • 52. 54 • Supervised learning – Decision tree induction – Linear regression – Logistic regression – Support vector machines & kernel methods – Model ensembles – Bayesian learning – Neural networks & deep learning – Learning theory • Unsupervised learning – Clustering – Dimensionality reduction • Reinforcement learning – Temporal difference learning – Q learning • Evaluation • Applications