2.12.
Think of different users for the database shown in Figure 1.2. What types of applications would
each user need? To which user category would each belong, and what type of interface would
each need?
2.13.
Choose a database application with which you
Lecture 10
Algorithms (contd)
Recursive Algorithm
n
Example: n! = ( n i ) = n (n-1)!
i =0
Original problem: Find n!
Decompose it into simpler subproblems. Find (n-1)!,
(n-2)!,.,0!=1. The solutions of these simplier problems
are combined to solve the origi
Hi, I'm Tucker Balch and
this is Computational Investing, Part I. This is the fourth module. In this module, we're gonna look
at what is a company worth? And the reason that's important
is that helps determine what ought the price of its stock be. Many ti
Hi again, I'm Tucker Balch, and
this is Computational Investing Part One. We're gonna continue talking about
the mechanics of the market. So when we left off last time,
we were talking about the order book. It's a little bit tricky, so I wanted to cover i
Hi there. Welcome to the last video in this module. I'm gonna talk now more
about hedge funds and the computing infrastructure
that they use. And this will form sort of the basis for
much of the rest of the class. We're not gonna be able to, in this class
Hi I'm Tucker Balch and
this is computational investing part one. For this video we're gonna
look at how to compute some of these values we had
talked about using Excel. So first we're gonna get some data,
load it into Excel and play with it a little bit
Hi, I'm Tucker Balch, and
this Computational Investing, Part I. We're continuing a discussion
of Mechanics of the Market. In this video,
I'm gonna talk a little bit about how hedge funds and other types of investment groups can take advantage of
some of t
[MUSIC] I'm Tucker Balch. We're here with Paul Jiganti
of TD Ameritrade. We've just been talking
about order flow and the fact that when a person makes
an order on their computer. It's tagged with a unique ID and from then on throughout the process
that u
[MUSIC] I'm Tucker Balch. I'm here with thinkorswim by
TD Ameritrade with Paul Jiganti and we're talking about how orders
flow through the system. Now, we started with a trailing stop order
that eventually became a limit order. It's made its way to a mark
Hi I'm Tucker Balch and
this is Computational Investing, part one. This is our third module. And in this module we're gonna look
at the mechanics of the markets. We need to understand
this computationally, in order to think about how are we gonna
apply co
Hi I'm Tucker Balch and we're continuing
with computational investing part one. We're in a module where we're looking at
different ways to assess hedge funds and hedge fund performance. We're gonna devote this session
to learning about Sharpe Ratio, which
CSCI 5203
LOGIC FOR COMPUTER SCIENCE
Name: Praveen Kumar Reddy Yeddula
Due date: 20th September 2016
Question 1
Let p(x) and Q(x) represents x is a rational number and x is a real number respectively. Symbolize
the following sentences
1.1 every rational n
CS3653 Assignment 3
Due March 13
This is a programming assignment. You have to write 4 short programs. The specs for each of these
programs are given below. You will handin these programs electronically before the beginning of the class
on the due date us
Lecture 14
Mathematical Logic
Logic its subject is reasoning; it measures the value of
reasoning; whether something is correct; it is a set of rules
to draw inferences.
Note: The form of logic is nothing to do with the content of
the subject to which it i
Lecture 9
Algorithms (contd)
Logarithms: R + R
y = log b x b y = x, b > 1 b is the "base".
Traditional base=10
Example 1:
log 4 16 = ?
y = log 4 16 16 = 4 y y = 2
log 2 16 16 = 2 y y = 4
4
2
0
2
Log2(x)
4
6
8
10
0
1
2
3
4
5
6
7
8
9
10
x
log b 1 = ?; 1 = b
Lecture 15
Mathematical Logic (Contd)
Generalization: let
p1 , p2 ,
, pn
be l.e.s that are the premises
of an argument and let q be an l.e. that is the conclusion.
Then
p1
.
.
pn
q
is valid iff
n
pi q
i =1
(1)
is a tautology.
Note:
An argument is vali
Lecture 13
Mathematical Induction (Contd)
Example 5: Knowing that
p(n):
2 is irrational, prove that
P (n) = 1+ 1+ 1+
n 1' s
is irrational,
n cfw_1, 2,3, .
1. Basis step:
P ( 2 ) = 1 + 1 = 2 is irrational, so
p(2) is true.
2. Assumption: Assume p(n) is tru
Lecture 11
Algorithms (contd)
Algorithm Analysis
From the last example we learned that we have to be
able to analyze the properties of an algorithm.
Main questions:
1) How to measure its performance?
how fast does it perform? (Number of iterations; in
eac
Lecture 12
Mathematical Induction
Induction:
A standard proof technique (most powerful and easy to
learn) for most theories and problems in discrete
mathematics; i.e., to verify the algorithms discussed in
Lectures 8 -10 are indeed correct.
Example 1:
n
5
Lecture 18
Graph Theory (contd)
Multigraph: Need to use some notation like the ones used
before for graphs;
Let G = (N, A)
A a multiset of ordered pairs from N N.
What is a multiset?
Definition: multiset A collection of objects (not
necessarily distinct).
Discrete Mathematics
Lecture 1:
Meaning and Motivation:
A kind of mathematics we as user (designer,
programmer), strongly need to know to communicate
with computers. It includes set theory, relations and
functions, vectors and matrices, probability, graph
Lecture 6
Probability (contd)
The rating of tails or heads to tosses, approaches , the
ratio stays the same if one consider, say every third toss.
This means: for every >0, may experimentally find the
number of times that
0.5 <
m
< + 0.5; m no. of time Ta
Lecture 7
Probability (contd)
Joint Probability:
For two events A & B that intersects in the sample space,
the probability p(AB) is called the Joint Probability.
A B
A
B
Venn Diagram
Shown that
p() = p(A) + p(B) - p(AB)
So, p(AB) = p(A) + p(B) - p() .(Joi
[MUSIC] I'm Tucker Balch. We're here at thinkorswim by
TD Ameritrade in Chicago. I'm here with Paul Jiganti who
thinkorswim's expert on market structure. And we're going to talk to with him about
how orders flow through their system. It's a very complex s