The document summarizes key points from Lecture 3 of an introduction to machine learning course. It discusses desired characteristics of machine learning techniques, including the ability to generalize but not too much, being robust, learning high-quality models, being scalable and efficient, being explanatory, and being deterministic. It also provides an overview of machine learning paradigms like inductive learning, explanation-based learning, analogy-based learning, evolutionary learning, and connectionist learning. Finally, it outlines specific problems that will be studied in the course, such as data classification, statistical learning, association analysis, and clustering.
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
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Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
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This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics – Also concerns how computational methods can aid the understanding of human language
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
sensors are what we experience the most in our life. they are even working in our body in different aspects. they may be as eyes, ears, skin, tongue etc. when we combine them they make a network. it may be a human sensor network. but i have shared something interesting about wireless sensor networks.
This presentation is all about the wireless sensor networks, how they collect data using aggregation, and how they evaluate or calculate the parameters
This slides about Wireless sensor network MAC protocol,
There are bunch of MAC protocol in research field.
It classify the MAC protocol and summarize the feature of typical sensor network MAC protcol
In early September, Apple released a paper describing Overton, the framework they built to create, monitor, and improve production-based ML systems. After presenting the main lines that define this framework, we will take a closer look at the heart of Overton: slice-based learning.
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Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
https://www.learntek.org/machine-learning-using-spark/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
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1. Introduction to Machine
Learning
Lecture 3
Albert Orriols i Puig
aorriols@salle.url.edu
i l @ ll ld
Artificial Intelligence – Machine Learning
Enginyeria i Arquitectura La Salle
gy q
Universitat Ramon Llull
2. Recap of Lecture 2
Machine learning
Learning = Improving with experience at some task
Improve over task T
With respect to a performance measure P
Based on experience E
Three especial niches
Data mining: extract information from historical data to help
g p
decision making
Software applications that are too complex to build a hard-
pp p
wired solution for
Self customizing p g
g programs
Slide 2
Artificial Intelligence Machine Learning
3. Today’s Agenda
Characteristics Desired for ML Methods
General issues
Concepts that will be used through lectures
Summary of the Paradigms that We Won’t
y g
Study
Summary of the P bl
S f th Problems th t W Will Study
that We St d
Slide 3
Artificial Intelligence Machine Learning
4. Characteristics Desired ML
We would like our ML techniques to have the following
q g
properties
Be able to generalize but not too much
generalize,
Be robust
Be li bl
B reliable
Learn models of high quality
Be scalable and efficient
Be explicative
Be determinist
Slide 4
Artificial Intelligence Machine Learning
5. Characteristics Desired ML
Be able to generalize, but not too much
g ,
We learn from a set of examples
Imagine that we are doing d t regression
I i th t d i data i
Examples (observations)
-- Real domain
Learned function
We only know the examples {e1, e2, e3, e4, e5, e6, e7, e8, e9}
We do not know the real distribution
So, does the learning function fits t e real d st but o
t e ea g u ct o ts the ea distribution?
Slide 5
Artificial Intelligence Machine Learning
6. Characteristics Desired ML
Be able to generalize, but not too much
g ,
Examples (observations)
-- Real domain
Learned function
What could have happened?
at cou d a e appe ed
I may not be a good representation of the original distribution
The ML method may not work well (overfitting)
So, what should we do?
Assume that I is a good representative of the original distribution
g p g
Go for the simplest solution
Slide 6
Artificial Intelligence Machine Learning
7. Characteristics Desired ML
Be robust
Real-world is imperfect and our measurements of real world
may be e e more imperfect
ay even o e pe ec
Therefore, we will deal with domains with
Noise
Uncertainty
Vagueness
We have to keep this in mind when designing our algorithms
Slide 7
Artificial Intelligence Machine Learning
8. Characteristics Desired ML
Learn models of high quality
gq y Test set
How do we evaluate learning quality? New instance
Information based Knowledge
on experience extraction
Learner Model
Dataset
Predicted Output
Training set
g
More advanced validation methods:
k-fold cross-validation
Holdout
Slide 8
Artificial Intelligence Machine Learning
9. Characteristics Desired ML
Be reliable
What do you prefer?
Do not predict something that you doubt about?
Or just bet for an option?
Classes are cost sensitive?
Cl t iti ?
What happens if I say that a patient, who has actually cancer,
is healthy?
What happens if I say that a patient, who is actually healthy,
has cancer?
Do I prefer to model one class as opposed to the other?
Fraud detection (0.1% of fraudulent transactions)
(% )
Geez, I modeled perfectly the non-fraudulent transactions!
Am I successful?
Slide 9
Artificial Intelligence Machine Learning
10. Characteristics Desired ML
Be scalable and efficient
Huge amount of data
Information hidden i th
If ti hidd in these d t
data
I need to process them quickly!
Two types o costs
o of costs:
Cost to build the model
Cost to classify new test examples
y p
Slide 10
Artificial Intelligence Machine Learning
11. Characteristics Desired ML
Be explicative
Should
Sh ld I care about giving an explanation?
b t ii l ti ?
Text/speech recognition
fast. huge,
Things happen too fast If errors are not too huge I do not care if
I read “a” instead of “e”
Medical diagnosis
g
I really care about obtaining an accurate explanation, since the
diagnosis may involve applying surgery to a patient or not
Slide 11
Artificial Intelligence Machine Learning
12. Characteristics Desired ML
Be determinist
If my data does not change
The learned model should be always the same
The answer for a given test instance should be always the
same
If my data changes
I should adapt to the changes
Slide 12
Artificial Intelligence Machine Learning
13. Paradigms in ML
Typically, techniques in ML have been divided in
different paradigms
Inductive learning
Explanation-based learning
p g
Analogy-based learning
Evolutionary learning
Connectionist Learning
Slide 13
Artificial Intelligence Machine Learning
14. Inductive Learning
Induce rules, trees or, in general, patterns from a set of
, ,g ,p
examples
Start from a specific experience
Draw inferences or generalizations from it
That is
Initial state: Original data
State: Symbolic description of the data with a certain degree of
generalization/specialization
Final state: Model with maximum generalization that implies the
input data
Slide 14
Artificial Intelligence Machine Learning
15. Explanation-Based Learning
Deduce information from a set of observations
Humans learn a lot from few examples
Machine: use results f
M hi lt from one example t solve th next
l to l the t
problem
Domain theory for the problem
EBL New domain theory
Goal concept
Training example
Slide 15
Artificial Intelligence Machine Learning
17. Explanation-based Learning
Example
Goal: Get to Brecon
Training data
Near (Cardiff, Brecon)
Airport (Cardiff)
Domain Knowledge
Near(x,y) ^ holds( loc(x), s ) holds( loc(y), result(drive(x,y),s) )
Airport(z) loc(z),
loc(z) result( fly(z), s )
fly(z)
Operational criterion: We must express concept definition in pure description
language syntax
Our goal can be expressed as
Holds ( loc(Brecon), s)
Slide 17
Artificial Intelligence Machine Learning
18. Learning Based on Analogy
A is similar to A’ according to α
α
A A’
If I have B, can I get B’?
Learn the causality relationship β β
β'
β
Transform α to α’
α'
B B’
Get B according to B and α’
B’ α
Where is the trick?
In learning α’ and β
Partial mapping
Previously
New Problem
solved problem
Derivation
Transformation
Solution to the Solution of this
problem known problem
Slide 18
Artificial Intelligence Machine Learning
19. Evolutionary Learning
Nature as problem solver
p
Nature evolved adapted solutions to life
Let’s
L t’ use thi concepts t learn f
this t to l from experience
i
Slide 19
Artificial Intelligence Machine Learning
20. Connectionist Learning
Mimic brain structure to build machines that are able to
learn
A brain consists of
Connected neurons that behave in a specific way
Let’s assume that this behavior can be coded functionally
Slide 20
Artificial Intelligence Machine Learning
21. Problems That We’ll Study
Typical ML courses go through the different families
yp g g
Structured courses
Big i t
Bi picture of th diff
f the different l
t learning paradigms
i di
However
Emergence of hybrid intelligent systems
Concepts come all mixed together
g
We are engineers. We need to solve problems
So,
So we propose to go problem-oriented
problem oriented
Techniques of different paradigms will come on our way
Slide 21
Artificial Intelligence Machine Learning
22. Problems That We’ll Study
Data classification: C4.5, kNN, Naïve Bayes …
1.
Statistical learning: SVM
2.
2
Association analysis: A-priori
3.
Link mining: Page Rank
4.
Clustering: k-means
g
5.
Reinforcement learning: Q-learning, XCS
6.
Regression
7.
7
Genetic Fuzzy Systems
8.
Slide 22
Artificial Intelligence Machine Learning
23. Next Class
How I Would Like my Problem to Look Like?
Summary of the Paradigms that we Won’t Study
Slide 23
Artificial Intelligence Machine Learning
24. Introduction to Machine
Learning
Lecture 3
Albert Orriols i Puig
aorriols@salle.url.edu
i l @ ll ld
Artificial Intelligence – Machine Learning
Enginyeria i Arquitectura La Salle
gy q
Universitat Ramon Llull