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Introduction to Machine Learning
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Machine Learning Step-by-Step Guide To Implement Machine Learning Algorithms with Python
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CHAPTER4
Machine Learning
4.1. INTRODUCTION TO LEARNING
Learning is the process to gather information and knowledge from past experience data
analysis and apply this information and knowledge to enhance the system performance. The
aim of learning or training a system is to acquire the necessary knowledge from the training
sample to make it able to differentiate among the regarded classes.
“Learning represents changes in a system that, make a system to do the same task more
efficiently the next time”.
“Learning is the process of constructing new or modifying existing representations of a
system according to experience to improve the efficiency of the system.”
There are three types of learning techniques, each corresponding to a particular type of
learning task. These are supervised learning, unsupervised learning and reinforcement
learning.
4.1.1. Supervised Learning
In supervised learning we provide an input and its corresponding target output to the
network, when inputs are given to the network, the network generate outputs and we compare
the network outputs to the target outputs. The learning function is then used to adjust the
biases of the network so that network outputs reach closer to the target outputs.
Learning
Training Algorithm Model Test Accuracy
Data Data
Step 1: Training Step 2: Testing
Fig. 4.1
Supervised learning is a machine learning technique used to learn a function from
training data set. The training data is a combination of input data and corresponding desired
outputs. The output of the function may be a continuous value or a classification of input
objects into classes. The main task of supervised learning is to find a function value that
,4.2 MACHINE LEARNING
produces the outputs that match our actual output for given input output data set. Supervised
learning is used for classification problems.
Supervised Learning Process
There are two steps in supervise learning process:
1. Learning (training): Learn a model using the training data.
2. Testing: Test the model using unseen test data to assess the model accuracy.
4.1.2. Unsupervised Learning
According to unsupervised learning, the weights and biases are modified with respect to
the network inputs only. In this type of learning no target outputs available therefore most
of these algorithm performed clustering operations. They categorised the input objects into
a diffrent classes. This technique is used in applications like vector quantization. In this
learning paradigm, suppose that we are given data samples without being told which classes
they belong to. There are schemes that are aimed to discover significant patterns in the input
data without a teacher.
In unsupervised learning, some data ‘x’ is given and the cost function is given. Our goal
is to minimize the cost in that function. The cost function is related to a problem for that we
want solution and may be related to a priori assumptions. For example, in data compression
problem it may be related to the mutual information between x and y, while in statistical
modeling problem, it may be related to the posterior probability of the model given the
data. Tasks that fall within this paradigm of unsupervised learning are in general estimation
problems; the applications include clustering, the estimation of statistical distributions,
compression and filtering.
4.1.3. Reinforcement Learning
Reinforcement learning is learning about how to map situations to the actions so as to
maximize the numerical reward signal. There are two main characteristics of reinforcement
learning are trial and error, delayed reward. You need to discover an action which must
produce most reward by hit and trial method. One important thing is that any action may
affect not only the intermediate reward but also next situation and all successor reward.
In reinforcement learning, data x are usually not given, data may produces at the time of
interactions of an agent with the environment. Whenever, the agent performs an action yt
and the environment generates an observation xt and an instantaneous cost ct, according to
some unknown dynamics. Our aim is to search a method for selecting actions that minimizes
the expected total cost. The environment’s dynamics and the total cost for each method are
generally unknown, but can be estimated. Reinforcement learning is better suits for control
problems, games and other sequential decision making tasks. There are two types of
Reinforcement learning:
Passive Reinforcement Learning: In fully observable environment, Passive learning
Policy is fixed (behavior does not change). The agent learns how good each state is. Similar
to policy evaluation, but Transition function and reward function or unknown. It is useful
for future policy revisions.
Active Reinforcement Learning: Using passive reinforcement learning, utilities of
states and transition probabilities are learned. Those utilities and transitions can be plugged
into Bellman equations. Bellman equations give optimal solutions given correct utility and
transition functions. Active reinforcement learning produces approximate estimates of those
functions.
, MACHINE LEARNING 4.3
4.1.4. Adaptation
Adaptation can be simply defined as a change in the relationship between recognized
pattern and the present classes that has been induced by the level of the pattern. A change by
which a pattern becomes better suited into its environment or classes. A major function of
adaptation is to increase the amount of sensor information for classifying a pattern into a
class. The amount of information collected depends upon the ways in which a samples
pattern and transducers signals. The amount of information that is used is further limited by
internal losses during transmission and processing. Adaptation can increase the information
of capturing and reduce internal losses by minimizing the effects of physical and biophysical
constraints.
4.2. DECISION TREES
A decision tree is a graphic display of various decision alternatives and the sequence of
events as if they were branches of a tree.
Rectangle Symbols are used to indicate decision points. And Circle Symbols are used to
denote situation of uncertainty or event branches coming out of a decision tree. These
points are representing of immediate mutually exclusive alternative open to decision maker.
A decision tree is highly useful to a decision point where immediate mutually exclusive
alternatives open to decision maker.
A decision tree is highly useful to a decision maker in multistage situation which
involve a serious of decisions each dependant on the preceding one.
Example 4.1. A company is running and after paying for materials labor etc. brings a
profit of Rs. 12000. The following alternatives are available to the company
1. The company can start a research R 1 which is coast of Rs 10000 having 90%
chances of success. If R1 successes the company gets total income of Rs 20000.
2. The company can start research R2 of coast of Rs 8000 having 60% chances of
success. If R2 successes the company gets total income of Rs 25000.
3. Company can pay Rs 6000 as royalty for a new process which will bring net gross
income Rs 20000.
4. The company continues the current process.
Because of limited recourse it is assumed that only one of the two researches can be
carried out at a time. Use decision tree analysis to locate the optimal strategy for the
company.
Solution. Following results we get from given decision tree: (Fig. 4.2)
1. If The company can conduct research R1. Net profit of company = 12500
2. If The company can conduct research R1. Net profit of company = 7000
3. If Company can pay Rs 6000 as royalty. Net profit = 14000.
4. If the company continues the current process. Net profit = 12000.
Hence final Decision is the option 3 i.e. company pay royalty.
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