Question we can have it predict future

    

    
    

    

    

Question 1

Give a definition of a learning system. What are the basic
components at the learning system? There are three different tasks in machine
learning (ML): supervised learning, unsupervised learning, and reinforcement
learning. How are they different? What kind of data is provided for each
learning task? How is reinforcement learning (RL) related to supervised
learning? How is RL related to unsupervised learning? Explain how to teach a
chess—playing program using supervised, unsupervised, and reinforcement
learning paradigms, respectively.

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Learning is any
practice by which a system improves Performance from experience. For example,
let take school as an example. School have teacher, books and other resources.
Students they have they get experience from their teacher, books and other
resources and as a result of this they improve their performance.

Basic components

                  
I.           
Performance

               
II.           
Task
(future task)

            
III.           
Training
experience (datasets)

Supervise means to observe and
direct the execution of a task. Means supervising a machine learning model that
might be able to produce classification regions. Teach the model by training it
with some data from a labeled dataset than load the model with knowledge so
that we can have it predict future instances. Generally speaking, the model is
trained on a labeled dataset, so it can predict the outcome of out of sample
data.

There is 2 type of supervised learning: classification and regression.

Unsupervised learning is exactly as
it sounds, let the model work on its own to discover information that may not be
visible for our eyes. It uses machine learning algorithms that extract
conclusions on unlabeled data. Unsupervised learning has more difficult
algorithms than supervised learning, since we know little to no information
about the data, or the outcomes that are to be expected. With unsupervised
learning, we’re looking to find things such as group, clusters perform density
estimation and dimensionality reduction. In supervised learning, however, we
know what kind of data we’re dealing with, since it is labelled data.

A system interacts with environment
to perform some task in return of this environment give some reward to the
system reward is either positive or negative on the base of this reward system
improve his performance and again perform task until system achieve maximum
positive reward. Reinforcement learning is something between supervised and
unsupervised learning it told us when we are wrong through negative reward but
it didn’t tell us which way to get maximum positive reward it should explore
all the possibility.

In comparison to supervised
learning, unsupervised learning has: fewer tests and fewer model that can be
used in order to ensure the outcome of the model is accurate. As such
unsupervised learning create a less controllable environment, as the machine is
creating outcomes for us. The biggest difference between supervised and
unsupervised learning is that supervised learning deals with labeled data while
unsupervised learning deals with unlabeled data. In supervised learning, we
have machine learning algorithms for classification, and regression.
Classification is the organization of labeled data and regression is the
prediction of trends in labeled data to determine future outcomes. In unsupervised
learning, we have clustering. Clustering is the analysis of patterns and
groupings of unlabeled data. Reinforcement learning is not exactly supervised
learning because it doesn’t rely strictly on labeled data. It actually relies
on “reward”. But it’s not unsupervised learning either, since we know straight
when we model our “learner” which is the estimated reward.

Supervised learning: Labeled Data.

Unsupervised learning: Unlabeled Data.

Reinforcement learning: Have no data we
construct a model that generates data based on reward.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Question 2

Consider-solving the problem of
character—image classification using a back-propagation learning neural
network. What is the model structure? What training data should be provided?
How does the neural network (model) learn from data? Explain the procedure for
learning.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Question 3

What is generalization?

We’ve mostly been talking about the
training data right so and classifiers and the algorithms use the training data
to build the predictors so the training data attributes X along with some targets
Y which could be a clear at a class or a number or something so that’s what we
used to train to build our model but then the reason we’re building this
predictor is that sometime tomorrow or in the future we’re going to get new
data and on that data.

What is over?tting in learning?

Overfitting is happened when you find or when you learn a predictor
that fits the training data a little bit to wealth so it’s usually happens when
your predictor the function that you’re predicting is complex enough and
flexible enough to fit any kind of sort of noise in the training data so those
are patters that are present in the training data that will not be present
tomorrow in the future data that you see so the when that happens you say you
over fit.

Explain the over?tting phenomena using the polynomial interpolation
problem. How can we avoid the over?tting problem in learning?

Debugging and diagnosing things they can go wrong with learning
algorithms will give you specific tool to recognize when overfitting. If we
think overfitting is occurring what can we do to address when we had 1 or 2
dimensional data so we could just plot the hypothesis and see what was going on
and select the appropriate degree polynomial. We could just plot hypothesis and
if it was fitting the sort of very wiggly function that goes all over the place
to predict and we could then use appropriate degree polynomial. So plotting the
hypothesis could be one way to try to decide what degree polynomial to use but
that does always work. In fact it when we have so many features it also becomes
much harder to plot the data and becomes much harder to visualize it to decide
what features to keep or not so concretely suppose if  we’re trying to predict housing prices
sometimes we can just have a lot of different features and all of these
features seem you know maybe they seem 
kind of useful but if we have a lot of features and very little training
data then overfitting can become a problem in order to address overfitting
there are two main options for things that we can do the first option is to try
to reduce the number of features concretely one thing we could do is manually
look through the list of features and use that to try to decide which are the
more important features and therefore which are the features we should keep and
which other features we should throw out there are algorithms for automatically
deciding which features the key and which features to throw out this idea of
reducing the number of features can work well and can reduce overfitting and
when we talk about model selection we’ll go into this in much greater depth but
the disadvantage is that by throwing away some of the features is also throwing
away some of the information you have about the problem for example maybe all of
those features are actually useful for predicting the price of a house so maybe
we don’t actually want to throw some of our information or throw some of our
features away. Regularization: we’re going to keep all the features but we’re
going to reduce the magnitude this method works well we’ll see when we have a
lot of features each of which contributes a little bit to predicting the value.

 

How can we avoid the overfitting problem in back-propagation neural
networks?

Downhill

If you have very uneven training and
test data structure, try to fix it. E.g. the share of classes zero and one in
both datasets should be equal.

You can also randomly drop some neurons during training.

For randomly stopping neurons u can use the l2 loss function

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Question 4

Give a list of machine learning models for supervised learning. How
are they different? What are their similarities? What representations do they
use? What ML methods use tree structures for representing their model? What
methods use graph or network representations? What methods use list structures
or rule sets for representing models?

o  
Support
Vector Machines

o  
linear
regression

o  
logistic
regression

o  
naive
Bayes

o  
linear
discriminant analysis

o  
decision
trees

o  
k-nearest
neighbor algorithm

o  
Neural
Networks (Multilayer perceptron).

 

Classification (1R,
Naive Bayes, Decision tree learning algorithm such as ID3 CART and so on)

Numeric Value Prediction

Decision tree

            continuous and categorical inputs. While decision trees classify
quickly, the time for building a tree may be higher than another type of
classifier Decision trees suffer from a problem of errors propagating
throughout a tree Decision trees can be used to help predict the future The
trees are easy to understand Decision trees work more efficiently with discrete
attributes The trees may suffer from error propagation

SVM

            continuous value inputs

Naïve Bayes  

            A simple but effective learning
system. Each piece of data that is to be classified consists of a set of
attributes, each of which can take on a number of possible values. The data are
then classified into a single classification. Advantages: –Fast to train
(single scan). Fast to classify

–Not
sensitive to irrelevant features –Handles real and discrete data –Handles
streaming data well Disadvantages: –Assumes independence of features

 

KNN

            continuous value inputs

 

 

 

o  
Decision
Trees are fast to train and easy to
evaluate and interrupt.

o  
Support
vector machine gives good accuracy, power of
flexibility from kernels.

o  
Neural
network is slow to converge and hard to set
parameters but if done with care it works wells

o  
Bayesian
classifiers are easy to understand.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Question 5

Machine learning methods can be de?ned by three dimensions: type of
learning data, model structure, and learning algorithm. Describe these aspects
for each of the following methods.

Linear regression

            Type
of learning data

            Continuous and categorical inputs

Model structure

Linear
regression is a very simple approach for supervised learning. Though it may
seem somewhat dull compared to some of the more modern algorithms, linear
regression is still a useful and widely used statistical learning method.
Linear regression is used to predict a quantitative response Y from the
predictor variable X.

Linear Regression is made with an
assumption that there’s a linear relationship between X and Y.

Y = W­­­­­0 + W1X,

where X is the explanatory
variable and Y is the dependent variable. The slope of the line is W­­­­­1,
and W­­­­­0 is the intercept (the value of y when x
= 0)

Learning
algorithm  

Multi-layer preceptors, Support vector
machine, Random forest

Decision trees

            Type
of learning data

            Continuous and categorical inputs

Model structure

A tree has many
analogies in real life, and turns out that it has influenced a wide area of
machine learning, covering both classification and regression. In decision
analysis, a decision tree can be used to visually and explicitly represent
decisions and decision making. As the name goes, it uses a tree-like model of
decisions. Though a commonly used tool in data mining for deriving a strategy
to reach a particular goal, it’s also widely used in machine learning, which
will be the main focus of this article. The goal of Decision Tree is to create
a model that predicts the value of a target variable by learning simple
decision rules inferred from the data features

Learning
algorithm  

Random forest,
C4.5, ID3, C5.0 etc.

Neural networks

            Type
of learning data

            Continuous and categorical inputs

Model structure

The dendrites
carry the signal to the cell body where they all get summed. If the final sum
is above a certain threshold, the neuron can fire, sending a spike along its
axon. In the computational model, we assume that the precise timings of the
spikes do not matter, and that only the frequency of the firing communicates
information. we model the firing rate of the neuron with an activation function
(e.g. sigmoid function)

Learning
algorithm

 

K-means clustering

            Type
of learning data

            Continuous inputs

Model structure

K-means
clustering is a type of unsupervised learning, which is used when you have
unlabeled data (i.e., data without defined categories or groups). The goal of
this algorithm is to find groups in the data, with the number of groups
represented by the variable K. The algorithm works iteratively to assign each
data point to one of K groups based on the features that are provided. Data
points are clustered based on feature similarity.

Rather than
defining groups before looking at the data, clustering allows you to find and
analyze the groups that have formed organically. The “Choosing K”
section below describes how the number of groups can be determined. 

Each centroid
of a cluster is a collection of feature values which define the resulting
groups. Examining the centroid feature weights can be used to qualitatively
interpret what kind of group each cluster represents.

Pizza example which you told us in
the class population divided according to 3 branches of pizza.

Learning
algorithm

Naive Bayes Classi?er

            Type
of learning data

Model structure

This lets us examine the probability of an
event based on the prior knowledge of any event that related to the former
event. So for example, the probability that price of a house is high, can be
better assessed if we know the facilities around it, compared to the assessment
made without the knowledge of location of the house. Bayes’ theorem does
exactly that.

 

 

Above
equation gives the basic representation of the Bayes’ theorem. Here A and B are
two events and, P(A|B): the conditional probability that event A occurs,
given that B has occurred. This is also known as the posterior probability.

P(A)
and P(B): probability of A and B without regard of each other.

P(B|A):
the conditional probability that event B occurs, given that A has occurred.

 

Learning
algorithm

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Question 6

Explain how machine learning can be used for the following
applications. You may refer to existing work and discuss it.

Optical character recognition

Predicting customer’s response to
coupon mails

Speech recognition

Autonomous car driving

Clustering the types of customers of
internet shopping malls

Board game playing (e.g., Chess,
backgammon)

Helicopter control

 

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