Artificial Intelligence Overview

AI OVERVIEW
https://opencubelabs.com

“The science and engineering of making
intelligent machines, especially intelligent
computer programs.”
-John McCarthy

AI HISTORY

AI APPROACHES
●
Logic or Rule Based
●
Machine Learning (Pattern Based)

MATHEMATICS FOR AI

“A person working in the field of AI who doesn’t
know math is like a politician who doesn’t know
how to persuade. Both have an inescapable area to
work upon!”

LINEAR ALGEBRA
●
Vectors
●
Matrices
●
Eigenvalues & Eigenvectors
●
Principle Component Analysis
●
Singular Value Decomposition

CALCULUS
●
Functions
●
Scalar Derivative
●
Gradient
●
Vector and Matrix Calculus
●
Gradient Algorithms

PROBABILITY
●
Basic Rules and Axioms
●
Random Variables
●
Bayes’ Theorem
●
Distributions: Binomial, Bernoulli, Poisson, Exponential, Gaussian
●
Conjugate Priors

AN AI METHODOLOGY

MACHINE LEARNING

TYPES OF LEARNING
●
Supervised Learning
●
Unsupervised Learning
●
Semi-supervised Learning

y = f(X)
X is the features/inputs
y is the target/output
f(X) is the learning function
Types :
●
Regression
●
Classification
SUPERVISED LEARNING

UNSUPERVISED LEARNING
●
We have input data (X) but no corresponding output variable (y).
●
The goal is to model the distribution of the data in order to learn more
about the data.
●
Types of unsupervised learning :
●
Clustering
●
Association

●
A form of predictive modelling technique which investigates the relationship
between a dependent (target) and independent variable (s) (predictor).
●
It is used for forecasting, time series modelling and finding the causal effect
relationship between the variables.
●
It indicates the significant relationships between dependent variable and
independent variable.
●
It indicates the strength of impact of multiple independent variables on a
dependent variable.
●
Types of regression : Linear, Logistic, Polynomial, Stepwise, Ridge, Lasso
and ElasticNet
REGRESSION

●
A classification problem is when the output variable is a category.
●
Examples : Emails filtering, Spam/Not Spam
CLASSIFICATION

●
The aim is to segregate groups with similar traits and assign them into
clusters.
●
Types of Clustering:
●
Hard Clustering: In hard clustering, each data point either belongs to a
cluster completely or not.
●
Soft Clustering: In soft clustering, instead of putting each data point into
a separate cluster, a probability or likelihood of that data point to be in
those clusters is assigned.
●
When we want to discover rules that describe portions of the input data it
is known as association problem.
CLUSTERING AND
ASSOCIATION

SUPERVISED LEARNING
ALGORITHMS

LINEAR REGRESSION
●
It is used to estimate real values (cost of houses, number of calls, total sales
etc.) based on continuous variable(s).
●
Here, we establish relationship between independent and dependent
variables by fitting a best line.
●
This best fit line is known as regression line and represented by a linear
equation
Y= a * X + b
Y – Dependent Variable
a – Slope
X – Independent variable
b – Intercept

LINEAR REGRESSION

LOGISTIC REGRESSION
●
It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false )
based on given set of independent variable(s).
●
It predicts the probability of occurrence of an event by fitting data to a logit function.

KNN
●
KNN can be used for both classification and regression problems.
●
It stores all available cases and classifies new cases by a majority vote of its k
neighbors.
●
KNN is computationally expensive.

NAIVE BAYES
●
Naive Bayes is a supervised learning algorithm which is based on bayes theorem.
●
The word naive comes from the assumption of independence among features.
●
We can write bayes theorem as follows :
Where,
P(x) is the prior probability of a feature.
P(x | y) is the probability of a feature given target. It's also known as likelihood.
P(y) is the prior probability of a target or class in case of classification.
p(y | x) is the posterior probability of target given feature.

SVMs
●
SVMs are among the best supervised learning algorithms.
●
It is effective in high dimensional space and it is memory efficient as well.
●
We plot each data item as a point in n-dimensional space andperform classification
by finding the hyperplane that differentiate the two classes very well.
●
We can draw m number of hyperplanes.
●
The optimal hyperplane is obtained by maximizing the margin.

DECISION TREE
●
Decision Tree is the supervised learning algorithm which can be used for
classification as well as regression problems.
●
Here we split population into set of homogeneous sets by asking set of questions.
●
Example : To decide what to do on a particular day.

DECISION TREE

RANDOM FOREST
●
Random Forest is the most common type of Ensemble Learning.
●
It is a collection of decision trees.
●
To classify a new object based on attributes, each tree gives a classification
and we say the tree “votes” for that class. The forest chooses the classification
having the most votes (over all the trees in the forest).
●
There are plethora of advantages of random forest such as they are fast to train,
requires no input preparation.
●
One of the disadvantage of random forest is that our model may become too large.

UNSUPERVISED LEARNING
ALGORITHMS

K MEANS CLUSTERING
●
K-means is one of the simplest unsupervised learning algorithm used for
clustering problem.
●
Our goal is to group objects based on their features similarity.
●
Basic idea behind K-means is, we define k centroids,
that is, one for each cluster.

CROSS VALIDATION
●
A method to test how well a model performs on unseen data.
●
Types of Cross Validation methods:
●
Hold out method
●
K-fold method
●
Leave-one-out cross validation

OVERFITTING AND
UNDERFITTING
●
Overfitting happens when a model performs too well on training data
but does not perform well on unseen data.
●
Underfitting when a model does not perform well on training data as
well as unseen data.

ARTIFICIAL NEURAL
NETWORK (ANN)

NEURAL NETWORK

NEURON

ACTIVATION FUNCTIONS
●
Step Function
●
Sigmoid Function
●
Tanh Function
●
Rectified Linear Unit (ReLU) Function

STEP FUNCTION

SIGMOID FUNCTION

TANH FUNCTION

RELU FUNCTION

FEEDFORWARD NEURAL
NETWORK

COST FUNCTION

GRADIENT DESCENT

BACKPROPAGATION

DEEP LEARNING

CONVOLUTIONAL
NEURAL NETWORKS
(CNN)

RECURRENT NEURAL
NETWORKS (RNN)

GENERATIVE ADVERSERIAL
NETWORKS (GANs)

“The most interesting idea in the last decade in Machine
Learning” - Yann LeCun

TRANSFER LEARNING

THANK YOU

Artificial Intelligence Overview

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