Unit-1 introduction to machine learning

Subject Coordinator : Dr. Ashima
Arya

Content
s
⚫Syllabus
⚫Text Books
⚫Course
Outcomes
⚫Unit-1

Text Books
1)Tom M. Mitchell, ―Machine Learning, McGraw-
Hill Education (India) Private Limited, 2013.
2)Ethem Alpaydin, ―Introduction to Machine
Learning (Adaptive Computation and Machine
Learning), The MIT Press 2004.
3)Stephen Marsland, ―Machine Learning:
An Algorithmic Perspective, CRC Press,
2009.
4)Bishop, C., Pattern Recognition and
Machine Learning. Berlin: Springer-

⚫“Artificial Intelligence, deep learning,
machine learning—whatever you’re
doing if you don’t understand it—learn
it. Because otherwise, you’re going to
be a dinosaur
within 3 years.”
- Mark Cuban, American
entrepreneur

What is Machine Learning?
⚫Artificial Intelligence is the concept of creating
smart intelligent machines.
⚫Machine Learning is a subset of artificial
intelligence that helps you build AI-driven
applications.
⚫Deep Learning is a subset of machine learning
that uses vast volumes of data and complex
algorithms to train a model.

⚫Let’s have a look at an example of an AI-driven
product
- Amazon Echo :

How Does Machine Learning Work?
⚫Machine learning accesses vast amounts of data
(both structured and unstructured) and learns
from it to predict the future. It learns from the
data by using multiple algorithms and
techniques.

ML Applications & Examples
1) Social Media Features : E.g : Facebook
notices & records your activities, chats, likes,
and comments, and the time you spend on
specific kinds of
posts. ML learns from your own experience &
makes friends & page suggestions for your
profile.

2) Product Recommendations Using ML &
AI, websites track your behavior based on
your previous purchases, searching patterns,
and cart history, and then make product
recommendations.

3) Image Recognition : Image recognition is an
approach for cataloging and detecting a feature or
an object in the digital image.

4) Sentiment Analysis : It is a real-time ML
application that determines the emotion or
opinion of the speaker or the writer. For e.g, if
someone has written a review or email (or any
form of a document), a sentiment analyzer will
instantly find out the actual thought and tone of
the text.

5)Language Translation : One of the most
common ML applications is language
translation. It plays a significant role in the
translation of one language to another. E.g:
websites can translate from one language to
another effortlessly.
6) Medical diagnosis :
Real-world examples for medical diagnosis:
⚫Assisting in formulating a diagnosis or
recommends a treatment option
⚫Oncology & pathology use ML to recognise
cancerous tissue

1) Supervised Learning
⚫In supervised learning technique, we train the
machines using the "labelled" dataset, and
based on the training, the machine predicts
the output
⚫The labelled data specifies that some of the
inputs are already mapped to the output.
⚫First, we train the machine with the input &
corresponding output, and then we ask the
machine to predict the output using the test
dataset.

Supervised Learning Techniques
Supervised machine learning can be classified
into two types of problems, which are given
below:
1) Classification A classification problem is
when the output variable is a category, such as
“Red” or “blue” , “disease” or “no disease”.
Some popular classification algorithms are given
below:
⚫Random Forest Algorithm
⚫Decision Tree Algorithm (ID3)
⚫Naïve Bayes Algorithm

Supervised Learning
2) Regression A regression problem is when the
output variable is a real value, such as “dollars”
or “weight”.
Techniques of Regression :
⚫Simple Linear Regression Algorithm
⚫Multivariate Regression Algorithm

Supervised Learning Example
⚫Suppose you are given a basket filled with
different kinds of fruits.

⚫Now the first step is to train the machine with
all the different fruits one by one like this:
⚫If the shape of the object is rounded and has
a depression at the top, is red in color, then it
will be labeled as –Apple.
⚫If the shape of the object is a long curving
cylinder having Green-Yellow color, then it will
be labeled as – Banana.
⚫Now suppose after training the data, you have
given a new separate fruit, say Banana from
the basket, and asked to identify it.

⚫Since the machine has already learned the
things from previous data and this time has to
use it wisely. It will first classify the fruit with its
shape and color and would confirm the fruit
name as BANANA and put it in the Banana
category. Thus the machine learns the things
from training data(basket containing fruits) and
then applies the knowledge to test data(new
fruit).

2) Unsupervised Learning
⚫ The main aim of the unsupervised learning algorithm is to
group or categories the unsorted dataset according to the
similarities, patterns, and differences.
⚫ Unsupervised Learning can be further classified into
two types, which are given below:
1) Clustering: Some of the popular clustering algorithms
are
given below:
⚫ K-Means Clustering algorithm
⚫ Mean-shift algorithm
⚫ DBSCAN Algorithm
⚫ Principal Component Analysis
2) Association Rule Mining

Unsupervised Learning Example
⚫Suppose machine is given an image having
both dogs and cats which it has never seen.
⚫

Unsupervised Learning Example
⚫Machine categorizes them according to their
similarities, patterns, and differences, i.e.,
we can easily categorize the above picture
into two parts.
⚫ First may contain all pics having dogs in
them and
⚫second part may contain all pics having cats
in them.
⚫Here you didn’t learn anything before, which
means no training data or examples.

Quiz
⚫Identify whether given scenarios uses
Supervised or Unsupervised Learning ?
A) FB Face Recognition
B) Netflix Recommends Movies
C) Analysing Fraud Detection

3) Reinforcement Learning
⚫Reinforcement Learning is a feedback-based
ML technique in which an agent learns to
behave in an environment by performing the
actions and seeing the results of actions.
⚫For each good action, the agent gets positive
feedback, and for each bad action, the
agent gets negative feedback or penalty.
⚫The agent learns automatically using feedbacks
without any labeled data, unlike supervised
learning.
⚫E.g : Game-playing, Robotics, etc.

Reinforcement Learning
⚫How a Robotic dog learns the movement of his arms is
an e.g of Reinforcement learning.
⚫Let's take an example of a maze environment that
the agent needs to explore.
⚫Environment: It can be anything such as a room,
maze, football ground, etc.
⚫Agent: An intelligent agent such as AI robot.

Well Posed Learning Problem
⚫A computer program is said to learn from
experience E with respect to some class of tasks
T and performance measure P, if its
performance in tasks T, as measured by P,
improves with experience E.
⚫Any problem can be segregated as well-posed
learning problem if it has three traits –
a) Task
b) Performance Measure
c) Experience

Well Posed Learning Problem E.g
⚫Certain examples that efficiently defines the
well- posed learning problem are –
⚫1) A checkers learning problem :
⚫Task – Playing checkers game
⚫Performance Measure – % of games won
against opponent
⚫Experience – playing implementation games
against itself

Well Posed Learning Problem E.g
2) Fruit Prediction Problem
⚫Task – forecasting different fruits for recognition
⚫Performance Measure – able to predict maximum
variety of fruits
⚫Experience – training machine with the largest
datasets of fruits images
3) Face Recognition Problem
⚫Task – predicting different types of faces
⚫Performance Measure – able to predict maximum
types of faces
⚫Experience – training machine with maximum
amount of datasets of different face images

Designing a Learning System
⚫For any learning system, we must be knowing the
three elements — T (Task), P (Performance
Measure), and E (Training Experience).
⚫Learning process starts with task T, performance
measure P and training experience E and objective is
to find an unknown target function. The target
function is an exact knowledge to be learned from
the training experience and its unknown.
⚫For example, in a case of credit approval,
Experience : Customer application records
Task : To classify whether the given customer
application is eligible for a loan.
So in this case, the training examples can be
represented as (x1,y1)(x2,y2)..(xn,yn) where X
represents customer application details and y
represents the status of credit approval.

⚫Target function to be learned in the credit approval
learning system is a mapping function f:X →y. This
function represents the exact knowledge defining
the relationship between input variable X and
output variable y.
⚫Next, the learning algorithms try to guess a
“hypothesis’’ function h(X) that approximates the
unknown f(.). A hypothesis is a function that best
describes the target & hypothesis set is the collection
of all the possible legal hypothesis. This is the set
from which the ML algorithm would determine the
best possible (only one) which would best describe
the target function or the outputs. The goal of the
learning process is to find the final hypothesis
that best approximates the unknown target
function.

⚫We will look into the checkers learning problem
and apply the above design choices. For a
checkers learning problem, the three elements
will be,
⚫1. Task T: To play checkers
2.Performance measure P: Total % of the game
won in the tournament.
3.Training experience E: A set of games played
against itself

1) Choosing Training Experience
a)Direct or Indirect Feedback
⚫ Direct : Individual checkers board states & correct
moves for each
⚫ Indirect : Moves sequences & final outcome of
various games
⚫ Learner faces problem of ‘Credit Assignment’
b) Degree to which learner controls
sequence of training examples
c) How well it represents distribution of
e.g’s over which
Final system performance will be measured

E.g –Checkers Game
⚫Task T: playing checkers
⚫ Performance measure P: percent of games
won in the world tournament
⚫ Training experience E: games played against
itself

2) Choosing Target Function
⚫To determine exactly what type of knowledge
will be learned and how this will be used by the
performance program
⚫E.g : checkers-playing program that can generate
the legal moves from any board state. The
program needs only to learn how to choose the
best move from among these legal moves.
⚫chooseMove V :
BM
where B : Legal Board
state M : set of
real no’s

Choosing Target Function
⚫What should be the value of V for any board
state?
a) If b is a final board state that is won then V(b)=
100
b) if b is a final board state that is lost, then V(b) =
-100
c) if b is a final board state that is drawn, then
V(b) = 0
d) if b is a not a final state in the game, then V(b) =
V(b’), where b' is the best final board state
that can be achieved starting from b and

3) Choosing Representation for
Target Function
⚫Allow program to represent using a large table
with a distinct entry specifying the value for each
distinct board state.
⚫ To represent using a collection of rules that match
against features of the board state, or
⚫a quadratic polynomial function of predefined
board features, or
⚫ Artificial neural network

Choosing Representation for
Target Function
⚫E.g In Checkers Problem, for any given
board state, the function V will be calculated
as a linear combination of the following
board features:
Xl: the number of black pieces on the
board X2: the number of red pieces on
the board X3: the number of black
kings on the board X4: the number of
red kings on the board
X5: the number of black pieces threatened by
red (i.e., which can be captured on red's next
turn)

Choosing Representation for
Target Function
⚫Thus, our learning program will represent
V(b) as a linear function of the form :
where Wo through W6 are numerical
coefficients, or weights, to be chosen by the
learning algorithm.
Learned values for the weights Wl through W6 will
determine the relative importance of the various
board features in determining the value of the
board

Partial design of checkers learning
program
⚫Task T: playing checkers
⚫ Performance measure P: % of games won in
the world tournament
⚫ Training experience E: games played against
itself
⚫Target function:
chooseMove V :
BM
where B : Legal Board
state M : set of real
no’s
⚫Target function
representation :

4) Choosing a Function Approximatio
Algorithm
⚫To learn a Target Function f, we need a set of
Training Examples.
⚫
⚫
Training Example
Representation : Ordered
Pair= (b, Vtrain(b) )
⚫E.g : Black won the
game Vtrain(b) =
+100
⚫ X2=
0,
⚫
b=
(X1=3,X2=0,X3=1,X4=0,X5=0,X6=0)
<(X1=3,X2=0,X3=1,X4=0,X5=0,X6=0) +100>

Choosing a Function
Approximation Algorithm
⚫There are 2 steps in this phase :
a) Estimating the Training
Values : Vtrain(b) 
V^(successor (b))
where V^ represents learners current
approximation to V It estimates that this move will
help/destroy opponent.
b) Adjusting the weights :
We use LMS (Least Mean Square )

Introduction to ML Approaches
1) Artificial Neural Network (ANN)
Artificial Neural Network is a deep learning
method that arose from the concept of the
human brain Biological Neural Networks.
⚫There are three layers in the network
architecture:
Input layer, hidden layer (more than one), and the
output layer.
⚫Because of the numerous layers its sometimes
referred to as the MLP (Multi-Layer Perceptron).
⚫Neural Networks learn by eg’s, they cannot be

Artificial Neural Networks E.g

Unit-1 introduction to machine learning

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