Women and tobacco:
A cross sectional study
from North India
Economic Analysis of Public
Policy
Under the Guidance of Sir Krishna M
Presented by Shubham Singh(2012B3A7466P)
Ashutosh Bhatt(2012B3A7792P)
Background
Indian Law &
Suggestive
Measure
Aim
Result

Polynomial Curve Fitting
BITS F464 Machine Learning
Navneet Goyal
Department of Computer Science, BITS-Pilani, Pilani Campus, India
Polynomial Curve Fitting
Seems a very trivial concept!
All of us know it well!
Why are we discussing it in Machine Learn

Classification:
Linear Models
Prof. Navneet Goyal
CS & IS
BITS, Pilani
Perceptron
Classification
By now, you are well aware of the classification problem
Assign an input vector x to one of the K discrete disjoint
classes, Ck (non-overlapping classes

Linear Regression &
Classification
Prof. Navneet Goyal
CS & IS
BITS, Pilani
Topics
Modeling
Predictive Modeling
Regression
Classification
Linear Models for Regression
Fundamentals of Modeling
Abstract representation of a real-world process
Y=

Logistic Regression
PR OFE SSOR N AVN E E T G OYAL
DE PAR T M E N T O F C OM PU T E R S C I E N C E ,
B I T S-PI LAN I , PI LAN I C AM PU S
Perceptron
Logistic Regression
A Classification Technique
Linear regression- approximates the r

BITS F464 - Machine Learning
Assignment #1
Weightage: 10%
Due Data: 18th October, 2016
You are required to identify a problem which you can solve using Machine Learning. The
main objective of this assignment is

Name:
IDNO:
BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
Department of Computer Science and Information Systems
I SEMESTER 2013-2014
th
BITS C464/BITS F464 Machine Learning
05 December, 2013
Comprehensive Examination
PART A (Closed Book) - Weightage 20

NAME:
nd
02 May, 2013
IDNO:
BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
I SEMESTER 2012-2013
BITS C464/BITS F464 Machine Learning
Comprehensive Examination
Weightage: 40%
Part A (Short Answer Questions, 10*2 = 20)
1. Pictorially illustrate (using 2D s

BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
I SEMESTER 2010-2011
28th February, 2011
1.
BITS C464/BITS F464 Machine Learning
Midsem Test (closed book)
Weightage: 25%
In below figure, samples falling in one of the closed curves, belongs to one class an

Machine Learning
BITS F464
Navneet Goyal
Department of Computer Science, BITS-Pilani, Pilani Campus, India
Introduction
List down the tasks which we humans can do better than
computers!
Introduction
Lets look at these incredible things that humans can do

Bayesian vs. Frequentist Approach to
Probabbility
PROF. N AVNEET GOYA L
CS & IS
BIT S, P ILA NI
Bayesian vs. Frequentist
Two main approaches to probability in Statistics:
Frequentist or Classical approach
Bayesian approach
The mater

Perceptron Example
Prof. Navneet Goyal
Perceptron Learning Algorithm
1. Select random sample from training set as input
2. If classification is correct, do nothing
3. If classification is incorrect, modify the weight
vector w using:
Repeat this procedure

Bayesian Classification
Dr. Navneet Goyal
BITS, Pilani
Bayesian Classification
What are Bayesian Classifiers?
Statistical Classifiers
Predict class membership
probabilities
Based on Bayes Theorem
Nave Bayesian Classifier
Computationally Simple
Comparable

ACTIVE LEARNING
Navneet Goyal
Slides developed using material from:
1. Simon Tong, ACTIVE LEARNING: THEORY AND APPLICATIONS.
Ph.D. dissertation, Stanford University, August, 2001.
2. Burr Settles. ACTIVE LEARNING LITERATURE SURVEY
.
Computer Sciences Tech

Machine Learning
BITS F464
Navneet Goyal
Department of Computer Science, BITS-Pilani, Pilani
Campus, India
Introduction
Introduction
Lets look at these incredible things that humans can
do:
1. Identifying a song by just listening to a very small
part of i

DIMENSIONALITY REDUCTION USING PCA & SVD
Prof. Navneet Goyal
CS & IS Department
BITS, Pilani
Methods for Dimensionality Reduction
Two main methods
Feature Selection
Feature Extraction
Methods for Dimensionality Reduction
Feature selection: Choosing k<d im

BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
Department of Computer Science and Information Systems
I SEMESTER 2013-2014
BITS C464/BITS F464 Machine Learning
th
05 December, 2013
Comprehensive Examination
Weightage: 40%
PART B (Open Book) - Weightage 2

BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
I SEMESTER 2013-2014
28th September, 2013
BITS C464/BITS F464 Machine Learning
Midsem Test (closed book)
Weightage: 25%
1. Model NAND ( AND) & NOR ( OR) using perceptron.
[1+1]
2. For each of the Boolean fun

BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
BITS C464 MACHINE LEARNING
I Semester 2014-2015
WORKSHEET #2
Linear Regression using Linear Basis Functions
OBJECTIVE: Linear Basis function Models
Different Basis Function Models as Polynomial, Gaussian, S

BIRLA INSTITUTE OF TECHNOLOGY & SCIENCE, PILANI
BITS C464 MACHINE LEARNING
I Semester 2014-2015
WORKSHEET #4
Classification using Perceptron Model
OBJECTIVE:
Single Layer Perceptron model
Learning using Gradient Descent
Parameter estimation using Delta Ru

Classification
Prof. Navneet Goyal
BITS, Pilani
Classification & Prediction
What is Classification?
What is Prediction?
Any relationship between the
two?
Supervised or Unsupervised?
Issues
Applications
Algorithms
Classifier Accuracy
Classification vs. Pre

Classification:
Linear Models
Prof. Navneet Goyal
CS & IS
BITS, Pilani
Classification
By now, you are well aware of the classification problem
Assign an input vector x to one of the K discrete disjoint
classes, Ck
Overlapping classes: Multi-label classifi

Machine Learning
BITS F464
Navneet Goyal
Department of Computer Science, BITS-Pilani, Pilani
Campus, India
Introduction
Introduction
Lets look at these incredible things that humans can
do:
1. Identifying a song by just listening to a very small
part of i

Langston,
Cognitive Psychology
Perceptron Learning
How does a perceptron acquire its
knowledge?
The question really is: How does a
perceptron learn the appropriate
weights?
Perceptron Learning
Remember
our features:
Taste
Sweet = 1, Not_Sweet = 0
Seeds
Ed

Preliminaries
Prof. Navneet Goyal
CS & IS
BITS, Pilani
Topics
Probability Theory
Decision Theory
Information Theory
Topics
Probability Theory
Decision Theory
Information Theory
Probability Theory
Key concept is dealing with uncertainty
- Due to noise on m

Some Interesting Problems
in Machine Learning
Navneet Goyal
Department of Computer Science, BITS-Pilani, Pilani
Campus, India
Some Interesting Problems
Age Invariant Face Recognition Problem
Video-based Identification (CCTV footages where
face is not vi

Bayesian Classification
Dr. Navneet Goyal
BITS, Pilani
Bayesian Classification
What are Bayesian Classifiers?
Statistical Classifiers
Predict class membership
probabilities
Based on Bayes Theorem
Nave Bayesian Classifier
Computationally Simple
Comparable