Installing Projection Toolkit for Motif Finding

If you are using a linux terminal, perform the following to install projection toolkit from source file.

1. Download the source files from the terminal by using wget command. It will download the file to the present working directory

$ wget

2. Unzip the tar bz2 by typing the following in the command line

$ bzip2 projection-toolkit-0.42.tar.bz2

$ tar -xxvf projection-toolkit-0.42.tar

3. Add the following line to shared-src/{,,,}

4. For 64-bit architectures, comment out the line with ARCH in Makeconfig file.

#ARCH = -march=pentium3 -fomit-frame-pointer

5. Run make specifying 64-bit architecture.
make MACHINE_TYPE=i686-m64
If installation is successful, executable files will appear in the topmost level of projection-toolkit-0.42 directory.
For more information read the README file in projection-toolkit-0.42/doc/

Letters of Note: Dear Einstein, Do Scientists Pray?

Letters of Note: Dear Einstein, Do Scientists Pray?.


In January of 1936, a young girl named Phyllis wrote to Albert Einstein on behalf of her Sunday school class, and asked, “Do scientists pray?” Her letter, and Einstein’s reply, can be read below.

(Source: Dear Professor Einstein; Image: Albert Einstein in 1947, via Life.)

The Riverside Church

January 19, 1936

My dear Dr. Einstein,

We have brought up the question: Do scientists pray? in our Sunday school class. It began by asking whether we could believe in both science and religion. We are writing to scientists and other important men, to try and have our own question answered.

We will feel greatly honored if you will answer our question: Do scientists pray, and what do they pray for?

We are in the sixth grade, Miss Ellis’s class.

Respectfully yours,



January 24, 1936

Dear Phyllis,

I will attempt to reply to your question as simply as I can. Here is my answer:

Scientists believe that every occurrence, including the affairs of human beings, is due to the laws of nature. Therefore a scientist cannot be inclined to believe that the course of events can be influenced by prayer, that is, by a supernaturally manifested wish.

However, we must concede that our actual knowledge of these forces is imperfect, so that in the end the belief in the existence of a final, ultimate spirit rests on a kind of faith. Such belief remains widespread even with the current achievements in science.

But also, everyone who is seriously involved in the pursuit of science becomes convinced that some spirit is manifest in the laws of the universe, one that is vastly superior to that of man. In this way the pursuit of science leads to a religious feeling of a special sort, which is surely quite different from the religiosity of someone more naive.

With cordial greetings,

your A. Einstein

Review of Linear Algebra for Machine Learning

Data is often represented by vectors with d scalar features

Definition 1. The inner product of two vectors x and y is

Note that   , that is

Definition 2.

The Euclidean Norm of vector x is ||x||

Note that

If ||x|| = 1, then x is normalized

Otherwise, we can normalize x to x’ as follows

Definition 3.

Vector x and y are orthogonal, if their scalar product is zero.

The angle between two vectors x and y is    if

Geometric interpretation is shown below

Note that the orthogonal projection of y onto x is

Introduction to Machine Learning

Machine Learning

is useful when

  1. the pattern exists.
  2. it is difficult to pin down the problem mathematically.
  3. you have a data.

Pattern is an entity vaguely defined that could be given a name


Examples of patterns are palindromes in a sequence, spatial configuration of pixels in character recognition, speech signal in spectrogram, the salary, age, and debt records in credit card applications.

Learning is a process by which parameters of a learning machine are modified through a continuous process of stimulation by the environment in which it is embedded.



In Figure 1, parameters of the learning machine are tweaked based from the error signal.

Learning Paradigm

  1. Supervised Learning -with the help of a teacher
  2. Unsupervised Learning – with the help of a critic

Why unsupervised learning is important?

It is important because it may lead to a  new pattern, thus leading to knowledge discovery.



A proof that the Halting Problem is undecidable

Geoffrey K. Pullum
(School of Philosophy, Psychology and Language Sciences, University of Edinburgh)

No general procedure for bug checks will do.
Now, I won’t just assert that, I’ll prove it to you.
I will prove that although you might work till you drop,
you cannot tell if computation will stop.

For imagine we have a procedure called P
that for specified input permits you to see
whether specified source code, with all of its faults,
defines a routine that eventually halts.

You feed in your program, with suitable data,
and P gets to work, and a little while later
(in finite compute time) correctly infers
whether infinite looping behavior occurs.

If there will be no looping, then P prints out ‘Good.’
That means work on this input will halt, as it should.
But if it detects an unstoppable loop,
then P reports ‘Bad!’ — which means you’re in the soup.

Well, the truth is that P cannot possibly be,
because if you wrote it and gave it to me,
I could use it to set up a logical bind
that would shatter your reason and scramble your mind.

Here’s the trick that I’ll use — and it’s simple to do.
I’ll define a procedure, which I will call Q,
that will use P’s predictions of halting success
to stir up a terrible logical mess.

For a specified program, say A, one supplies,
the first step of this program called Q I devise
is to find out from P what’s the right thing to say
of the looping behavior of A run on A.

If P’s answer is ‘Bad!’, Q will suddenly stop.
But otherwise, Q will go back to the top,
and start off again, looping endlessly back,
till the universe dies and turns frozen and black.

And this program called Q wouldn’t stay on the shelf;
I would ask it to forecast its run on itself.
When it reads its own source code, just what will it do?
What’s the looping behavior of Q run on Q?

If P warns of infinite loops, Q will quit;
yet P is supposed to speak truly of it!
And if Q’s going to quit, then P should say ‘Good.’
Which makes Q start to loop! (P denied that it would.)

No matter how P might perform, Q will scoop it:
Q uses P’s output to make P look stupid.
Whatever P says, it cannot predict Q:
P is right when it’s wrong, and is false when it’s true!

I’ve created a paradox, neat as can be —
and simply by using your putative P.
When you posited P you stepped into a snare;
Your assumption has led you right into my lair.

So where can this argument possibly go?
I don’t have to tell you; I’m sure you must know.
A reductio: There cannot possibly be
a procedure that acts like the mythical P.

You can never find general mechanical means
for predicting the acts of computing machines;
it’s something that cannot be done. So we users
must find our own bugs. Our computers are losers!