Department of Mathematics
MTL 106 (Introduction to Probability and Stochastic Processes)
Tutorial Sheet No. 3
1. Let X be uniformly distributed random variable over the interval [0, 10]. Find the CDF
Department of Mathematics
MTL 106 (Introduction to Probability and Stochastic Processes)
Tutorial Sheet No. 2
1. Consider a probability space (, F , P ) with = cfw_0, 1, 2, F = cfw_, cfw_0, cfw_1, 2,
Department of Mathematics
MTL 106 (Introduction to Probability and Stochastic Processes)
Tutorial Sheet No. 1
1. Let = cfw_a, b, c, d. Find three different -fields cfw_Fn for n = 0, 1, 2 such that F0
MAL 732: Financial Mathematics, Practice Sheet 2
No Arbitrage Principle
1. Spot the chance of risk free prot without initial investment (arbitrage opportunity) in the following situation:
Suppose that
MAL 732: Financial Mathematics, Practice Sheet 3
Derivatives Pricing
1. The risk free interest is 7% per annum with continuous compounding. The dividend yield on a stock is 3.2% per
annum. The current
MAL 732: Financial Mathematics, Practice Sheet 6
Probability Measure, Expectation, Martingale
1. Consider the innite coin-toss space which is uncountably innite. Let
A = cfw_ = 1 2 3 4 5 . . . | 1 = 2
MAL 732: Financial Mathematics, Practice Sheet 4
Binomial Model and Black-Scholes Pricing
1. Let the current stock price be 120 and there are only two possibilities of its change: go up by 25% with
pr
MAL 732: Financial Mathematics, Practice Sheet 1
1. If you wish to accumulate Rs 140,000 in 10 years, how much must you deposit today in an account that pays an
annual interest rate of 8%?
2. At what
Association Analysis: Basic Concepts
and Algorithms
1
Association Rule Mining
Given a set of transactions, find rules that will predict the occurrence of an item
based on the occurrences of other item
Classification: Basic Concepts
and Decision Trees
1
Classification: Definition
Given a collection of records (training set )
Each record contains a set of attributes, one of
the attributes is the cla
Lect 2. Getting to Know Your Data ( Based on the book
Data Mining by Han, Kamber and Pei
Data Objects and Attribute Types
Basic Statistical Descriptions of Data
Data Visualization
Measuring Data S
Graph Partitioning
Dr. Frank McCown
Intro to Web Science
Harding University
This work is licensed under Creative
Commons Attribution-NonCommercial 3.0
Slides use figures from Ch 3.6 of
Networks, Crowd
( based on Han, Kimber and Peis book
Data Mining
1
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining
What Kinds of Data Can Be Mined?
What Kinds of Patterns Can Be Mined?
W
Nave Bayes Classification
Things Wed Like to Do
Spam Classification
Given an email, predict whether it is spam or not
Medical Diagnosis
Given a list of symptoms, predict whether a patient has
dise
Rule Based Classification
Rule-Based Classifier
Classify records by using a collection of ifthen rules
Rule: (Condition) y
where
Condition is a conjunctions of attributes
y is the class label
LH
DATA MINING
MTL 782
OVERVIEW
DBSCAN clustering algorithm
Methods for Cluster Validity
DBSCAN
DBSCAN
is a density-based algorithm.
It stands for Density-based spatial clustering of applications
with n
Nearest-Neighbor
Classifier
MTL 782
IIT DELHI
Instance-Based Classifiers
Set of Stored Cases
Atr1
.
AtrN
Class
A
Store the training records
Use training records to
predict the class label of
unseen
Classification through
Artificial Neural Networks
Data Mining
MTL 782
IIT Delhi
Overview of the presentation
Brief History & Applications of Artificial Neural Networks (ANN)
Feed-forward ANN
Learni
All the important algebra formulas for class 11 are listed below, to ease
students to search for them. This list will help students not to miss any
formula while studying for competitive or board exam
1
Lecture 7: Channel coding theorem for
discrete-time continuous memoryless channel
Lectured by Dr. Saif K. Mohammed
Scribed by Mirsad Cirki
c
Information Theory for Wireless Communication (ITWC) Spri
1
Information Theory for Wireless Communication
Lecture 3: Conditional Typicality, Channel Coding Theorem for DMC
Lecture by Dr. Saif K. Mohammed
Scribe by Hien Quoc Ngo
In this lecture, we introduce
1
Information Theory for Wireless Communications,
Part II:
Lecture 5: Multiuser Gaussian MIMO Multiple-Access Channel
Instructor: Dr. Saif K. Mohammed
Scribe: Johannes Lindblom
In this lecture, we giv
Easy Travel
Customer Information Sheet - Individual
www. apollomunichinsurance.com
The information mentioned below is illustrative and not exhaustive. Information must be read in conjunction with the
Ifa =xi^+yj^+zk^ then magnitude or length or norm or absolute value
of a is a=a=x2+y2+z2
A vector of unit magnitude is unit vector. If a is a vector then unit vector of
by a^ and a^=a^|a^| Therefore a