Lec 1 | MIT 6.00 Introduction to Computer Science and Programming, Fall 2008


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MIT OpenCourseware, at ocw.mit.edu . PROFESSOR: Good morning. Try it again. Good morning. STUDENTS: Good morning. PROFESSOR: Thank you. This is 6.00, also known as
Introduction to Computer Science and Programming. My name is Eric Grimson, I have
together Professor John Guttag over here, we’re
going to be lecturing the course this term. I want to give you a heads up;
you’re getting some serious firepower this term. John was department head for
ten years, felt like a century, and in course six,
I’m the current department head in course six. John’s been lecturing for
thirty years, roughly. All right, I’m the young guy,
I’ve only been lecturing for twenty-five years. You can tell, I have less
grey hair than he does. What I’m trying to say to
you is, we take this course really seriously. We hope you do as well. But we think it’s really
important for the department to help everybody learn about
computation, and that’s what this course is about. What I want to do today is three
things: I’m going to start– actually, I shouldn’t
say start, I’m going to do a little bit of administrivia, the
kinds of things you need to know about how we’re going
to run the course. I want to talk about the goal
of the course, what it is you’ll be able to do at the end
of this course when you get through it, and then I want
to begin talking about the concepts and tools of
computational thinking, which is what we’re primarily going
to focus on here. We’re going to try and help you
learn how to think like a computer scientist, and we’re
going to begin talking about that towards the end of this
lecture and of course throughout the rest of the
lectures that carry on. Right, let’s start
with the goals. I’m going to give you
goals in two levels. The strategic goals are the
following: we want to help prepare freshmen and sophomores
who are interested in majoring in course six to
get an easy entry into the department, especially for those
students who don’t have a lot of prior programming
experience. If you’re in that category,
don’t panic, you’re going to get it. We’re going to help you ramp
in and you’ll certainly be able to start the course six
curriculum and do just fine and still finish on target. We don’t expect everybody to
be a course six major, contrary to popular opinion,
so for those are you not in that category, the second thing
we want to do is we want to help students who don’t plan
to major in course six to feel justifiably confident in
their ability to write and read small pieces of code. For all students, what we want
to do is we want to give you an understanding of the role
computation can and cannot play in tackling technical
problems. So that you will come away with a sense of what
you can do, what you can’t do, and what kinds of things you
should use to tackle complex problems. And finally, we want to position
all students so that you can easily, if you like,
compete for things like your office and summer jobs. Because you’ll have an
appropriate level of confidence and competence
in your ability to do computational problem solving. Those are the strategic goals. Now, this course is primarily
aimed at students who have little or no prior programming
experience. As a consequence, we believe
that no student here is under-qualified for this
course: you’re all MIT students, you’re all qualified
to be here. But we also hope that there
aren’t any students here who are over-qualified
for this course. And what do I mean by that? If you’ve done a lot prior
programming, this is probably not the best course for you,
and if you’re in that category, I would please
encourage you to talk to John or I after class about what your
goals are, what kind of experience you have, and how
we might find you a course that better meets your goals. Second reason we don’t want
over-qualified students in the class, it sounds a little nasty,
but the second reason is, an over-qualified student,
somebody who’s, I don’t know, programmed for Google for the
last five years, is going to have an easy time in this
course, but we don’t want such a student accidentally
intimidating the rest of you. We don’t want you to feel
inadequate when you’re simply inexperienced. And so, it really is a course
aimed at students with little or no prior programming
experience. And again, if you’re not in that
category, talk to John or I after class, and we’ll help
you figure out where you might want to go. OK. Those are the top-level
goals of the course. Let’s talk sort of at a more
tactical level, about what do we want you to know
in this course. What we want you to be able
to do by the time you leave this course? So here are the skills that we
would like you to acquire. Right, the first skill we want
you to acquire, is we want you to be able to use the basic
tools of computational thinking to write small scale
programs. I’m going to keep coming back to that idea,
but I’m going to call it computational thinking. And that’s so you can write
small pieces of code. And small is not derogatory
here, by the way, it just says the size of things you’re
going to be able to do. Second skill we want you to have
at the end of this course is the ability to use a
vocabulary of computational tools in order to be
able to understand programs written by others. So you’re going to be able
to write, you’re going to be able to read. This latter skill, by the way,
is incredibly valuable. Because you won’t want to do
everything from scratch yourself, you want to be able
to look at what is being created by somebody else and
understand what is inside of there, whether it works
correctly and how you can build on it. This is one of the
few places where plagiarism is an OK thing. It’s not bad to, if you like,
learn from the skills of others in order to create
something you want to write. Although we’ll come back
to plagiarism as a bad thing later on. Third thing we want you to
do, is to understand the fundamental both capabilities
and limitations of computations, and the costs
associated with them. And that latter statement sounds
funny, you don’t think of computations having
limits, but they do. There’re some things that
cannot be computed. We want you to understand
where those limits are. So you’re going to be
able to understand abilities and limits. And then, finally, the last
tactical skill that you’re going to get out of this course
is you’re going to have the ability to map scientific
problems into a computational frame. So you’re going to be able to
take a description of a problem and map it into
something computational. Now if you think about
it, boy, it sounds like grammar school. We’re going to teach you to
read, we’re going to teach you to write, we’re going to teach
you to understand what you can and cannot do, and most
importantly, we’re going to try and give you the start
of an ability to take a description of a problem from
some other domain, and figure out how to map it into that
domain of computation so you can do the reading and writing
that you want to do. OK, in a few minutes we’re going
to start talking then about what is computation, how
are we going to start building those tools, but that’s what you
should take away, that’s what you’re going to gain out
of this course by the time you’re done. Now, let me take a sidebar for
about five minutes to talk about course administration, the
administrivia, things that we’re going to do in the course,
just so you know what the rules are. Right, so, class is two hours
of lecture a week. You obviously know where
and you know when, because you’re here. Tuesdays and Thursdays
at 11:00. One hour of recitation a week,
on Fridays, and we’ll come back in a second to how you’re
going to get set up for that. And nine hours a week of
outside-the-class work. Those nine hours are going to
be primarily working on problem sets, and all the
problems sets are going to involve programming in Python,
which is the language we’re going to be using this term. Now, one of the things you’re
going to see is the first problem sets are pretty easy. Actually, that’s probably
wrong, John, right? They’re very easy. And we’re going to ramp up. By the time you get to the end
of the term, you’re going to be dealing with some fairly
complex things, so one of the things you’re going to see is,
we’re going to make heavy use of libraries, or code
written by others. It’ll allow you to tackle
interesting problems I’ll have you to write from scratch, but
it does mean that this skill here is going to be
really valuable. You need to be able to read that
code and understand it, as well as write your own. OK. Two quizzes. During the term, the dates have
already been scheduled. John, I forgot to look them up,
I think it’s October 2nd and November 4th, it’ll be
on the course website. My point is, go check the course
website, which by the way is right there. If you have, if you know you
have a conflict with one of those quiz dates now, please
see John or I right away. We’ll arrange something
ahead of time. But if you– The reason I’m saying that is,
you know, you know that you’re getting married that day for
example, we will excuse you from the quiz to get married. We’ll expect you come right
back to do the quiz by the way, but the– Boy, tough crowd. All right. If you have a conflict,
please let us know. Second thing is, if you have an
MIT documented special need for taking quizzes, please see
John or I well in advance. At least two weeks
before the quiz. Again, we’ll arrange for this,
but you need to give us enough warning so that we can
deal with that. OK, the quizzes are open book. This course is not
about memory. It’s not how well you can
memorize facts: in fact, I think both John and I are a
little sensitive to memory tests, given our age,
right John? This is not about how you
memorize things, it’s about how you think. So they’re open note,
open book. It’s really going to test
your ability to think. The grades for the course will
be assigned roughly, and I use the word roughly because we
reserve the right to move these numbers around a little
bit, but basically in the following percentages: 55% of
your grade comes from the problem sets, the other 45%
come from the quizzes. And I should’ve said there’s two
quizzes and a final exam. I forgot, that final exam
during final period. So the quiz percentages
are 10%, 15%, and 20%. Which makes up the other 45%. OK. Other administrivia. Let me just look through
my list here. First problem set, problem set
zero, has already been posted. This is a really easy one. We intend it to be a really
easy problem set. It’s basically to get you to
load up Python on your machine and make sure you understand
how to interact with it. The first problem set will be
posted shortly, it’s also pretty boring– somewhat like
my lectures but not John’s– and that means, you know,
we want you just to get going on things. Don’t worry, we’re going to make
them more interesting as you go along. Nonetheless, I want to stress
that none of these problems sets are intended
to be lethal. We’re not using them to weed you
out, we’re using them to help you learn. So if you run into a problem
set that just, you don’t get, all right? Seek help. Could be psychiatric help,
could be a TA. I recommend the TA. My point being, please come
and talk to somebody. The problems are set up so that,
if you start down the right path, it should be pretty
straight-forward to work it through. If you start down a plausible
but incorrect path, you can sometimes find yourself stuck in
the weeds somewhere, and we want to bring you back in. So part of the goal here is,
this should not be a grueling, exhausting kind of task, it’s
really something that should be helping you learn
the material. If you need help, ask John,
myself, or the TAs. That’s what we’re here for. OK. We’re going to run primarily a
paperless subject, that’s why the website is there. Please check it, that’s where
everything’s going to be posted in terms of things
you need to know. In particular, please go to it
today, you will find a form there that you need to fill out
to register for, or sign up for rather, a recitation. Recitations are on Friday. Right now, we have them
scheduled at 9:00, 10:00, 11:00, 12:00, 1:00, and 2:00. We may drop one of the
recitations, just depending on course size, all right? So we reserve the right,
unfortunately, to have to move you around. My guess is that 9:00 is not
going to be a tremendously popular time, but maybe
you’ll surprise me. Nonetheless, please
go in and sign up. We will let you sign up for
whichever recitation makes sense for you. Again, we reserve the right to
move people around if we have to, just to balance load, but we
want you to find something that fits your schedule
rather than ours. OK. Other things. There is no required text. If you feel exposed without a
text book, you really have to have a textbook, you’ll find one
recommended– actually I’m going to reuse that word, John,
at least suggest it, on the course website. I don’t think either of us are
thrilled with the text, it’s the best we’ve probably found
for Python, it’s OK. If you need it, it’s there. But we’re going to basically not
rely on any specific text. Right. Related to that: attendance
here is obviously not mandatory. You ain’t in high
school anymore. I think both of us would love to
see your smiling faces, or at least your faces,
even if you’re not smiling at us every day. Point I want to make about this,
though, is that we are going to cover a lot of material
that is not in the assigned readings, and we do
have assigned readings associated with each one
of these lectures. If you choose not to show up
today– or sorry, you did choose to show up today, if you
choose not to show up in future days– we’ll understand,
but please also understand that the TAs won’t
have a lot of patience with you if you’re asking a question
about something that was either covered in the
readings, or covered in the lecture and is pretty
straight forward. All right? We expect you to behave
responsibly and we will as well. All right. I think the last thing I want
to say is, we will not be handing out class notes. Now this sounds like a
draconian measure; let me tell you why. Every study I know of, and I
suspect every one John knows, about learning, stresses that
students learn best when they take notes. Ironically, even if they
never look at them. OK. The process of writing is
exercising both halves of your brain, and it’s actually helping
you learn, and so taking notes is really
valuable thing. Therefore we’re not going
to distribute notes. What we will distribute for
most lectures is a handout that’s mostly code examples
that we’re going to do. I don’t happen to have one today
because we’re not going to do a lot of code. We will in future. Those notes are going to make
no sense, I’m guessing, outside of the lecture,
all right? So it’s not just, you can swing
by 11:04 and grab a copy and go off and catch
some more sleep. What we recommend is you use
those notes to take your own annotations to help you
understand what’s going on, but we’re not going to
provide class notes. We want you to take your own
notes to help you, if you like, spur your own
learning process. All right. And then finally, I want to
stress that John, myself, all of the staff, our job is
to help you learn. That’s what we’re here for. It’s what we get
excited about. If you’re stuck, if you’re
struggling, if you’re not certain about something,
please ask. We’re not mind readers, we
can’t tell when you’re struggling, other than sort of
seeing the expression on your face, we need your help
in identifying that. But all of the TAs, many of whom
are sitting down in the front row over here, are here
to help, so come and ask. At the same time, remember that
they’re students too. And if you come and ask a
question that you could have easily answered by doing the
reading, coming to lecture, or using Google, they’re going
to have less patience. But helping you understand
things that really are a conceptual difficulty is what
they’re here for and what we’re here for, so please
come and talk to us. OK. That takes care of the
administrivia preamble. John, things we add? PROFESSOR GUTTAG: Two
more quick things. This semester, your class
is being videotaped for OpenCourseware. If any of you don’t want your
image recorded and posted on the web, you’re supposed to sit
in the back three rows. PROFESSOR GRIMSON:
Ah, thank you. I forgot. PROFESSOR GUTTAG: –Because
the camera may pan. I think you’re all very
good-looking and give MIT a good image, so please, feel
free to be filmed. PROFESSOR GRIMSON: I’ll turn
around, so if you want to, you know, move to the back,
I won’t see who moves. Right. Great. Thank you, John. PROFESSOR GUTTAG: So that, the
other thing I want to mention is, recitations are also
very important. We will be covering material in
recitations that’re not in the lectures, not in the
reading, and we do expect you to attend recitations. PROFESSOR GRIMSON: Great. Thanks, John. Any questions about
the administrivia? I know it’s boring, but we need
to do it so you know what the ground rules are. Good. OK. Let’s talk about computation. As I said, our strategic goal,
our tactical goals, are to help you think like a computer
scientist. Another way of saying it is, we want to give
you the skill so that you can make the computer do what
you want it to do. And we hope that at the end of
the class, every time you’re confronted with some technical
problem, one of your first instincts is going to be, “How
do I write the piece of code that’s going to help
me solve that?” So we want to help you
think like a computer scientist. All right. And that, is an interesting
statement. What does it mean, to think
like a computer scientist? Well, let’s see. The primary knowledge you’re
going to take away from this course is this notion of
computational problem solving, this ability to think in computational modes of thought. And unlike in a lot of
introductory courses, as a consequence, having the ability
to memorize is not going to help you. It’s really learning those
notions of the tools that you want to use. What in the world does
it mean to say computational mode of thought? It sounds like a hifalutin
phrase you use when you’re trying to persuade
a VC to fund you. Right. So to answer this, we really
have to ask a different question, a related question;
so, what’s computation? It’s like a strange
statement, right? What is computation? And part of the reason for
putting it up is that I want to, as much as possible,
answer that question by separating out the mechanism,
which is the computer, from computational thinking. Right. The artifact should not be
what’s driving this. It should be the notion of,
“What does it mean to do computation?” Now, to answer that, I’m going
to back up one more level. And I’m going to pose what
sounds like a philosophy question, which is, “What is
knowledge?” And you’ll see in about two minutes why I’m
going to do this. But I’m going to suggest that I
can divide knowledge into at least two categories. OK, and what is knowledge? And the two categories I’m going
to divide them into are declarative and imperative
knowledge. Right. What in the world is declarative
knowledge? Think of it as statements
of fact. It’s assertions of truth. Boy, in this political season,
that’s a really dangerous phrase to use, right? But it’s a statement of fact. I’ll stay away from the
political comments. Let me give you an
example of this. Right. Here’s a declarative
statement. The square root of x is that y
such that y squared equals x, y’s positive. You all know that. But what I want you to
see here, is that’s a statement of fact. It’s a definition. It’s an axiom. It doesn’t help you
find square roots. If I say x is 2, I want to know,
what’s the square root of 2, well if you’re enough of
a geek, you’ll say 1.41529 or whatever the heck it is, but in
general, this doesn’t help you find the square root. The closest it does is it would
let you test. You know, if you’re wandering through
Harvard Square and you see an out-of-work Harvard grad,
they’re handing out examples of square roots, they’ll give
you an example and you can test it to see, is the
square root of 2, 1.41529 or whatever. I don’t even get laughs at
Harvard jokes, John, I’m going to stop in a second
here, all right? All right, so what am I
trying to say here? It doesn’t — yeah, exactly. We’re staying away from that,
really quickly, especially with the cameras rolling. All right. What am I trying to say? It tells you how you might test
something but it doesn’t tell you how to. And that’s what imperative
knowledge is. Imperative knowledge
is a description of how to deduce something. So let me give you an
example of a piece of imperative knowledge. All right, this is actually a
very old piece of imperative knowledge for computing square
roots, it’s attributed to Heron of Alexandria, although I
believe that the Babylonians are suspected of knowing
it beforehand. But here is a piece of
imperative knowledge. All right? I’m going to start with a guess,
I’m going to call it g. And then I’m going to say, if g
squared is close to x, stop. And return g. It’s a good enough answer. Otherwise, I’m going to get a
new guess by taking g, x over g, adding them, and
dividing by two. Then you take the average
of g and x over g. Don’t worry about how came
about, Heron found this out. But that gives me a new guess,
and I’m going to repeat. That’s a recipe. That’s a description
of a set of steps. Notice what it has, it has a
bunch of nice things that we want to use, right? It’s a sequence of specific
instructions that I do in order. Along the way I have some tests,
and depending on the value of that test, I may change
where I am in that sequence of instructions. And it has an end test,
something that tells me when I’m done and what
the answer is. This tells you how to
find square roots. it’s how-to knowledge. It’s imperative knowledge. All right. That’s what computation
basically is about. We want to have ways of
capturing this process. OK, and that leads now to an
interesting question, which would be, “How do I build a
mechanical process to capture that set of computations?” So
I’m going to suggest that there’s an easy way to do it– I realized I did the boards in
the wrong order here– one of the ways I could do it is, you
could imagine building a little circuit to do this. If I had a couple of elements of
stored values in it, I had some wires to move things
around, I had a little thing to do addition, little thing
to do division, and a something to do the testing, I
could build a little circuit that would actually do
this computation. OK. That, strange as it sounds, is
actually an example of the earliest computers, because the
earliest computers were what we call fixed-program
computers, meaning that they had a piece of circuitry
designed to do a specific computation. And that’s what they would do:
they would do that specific computation. You’ve seen these
a lot, right? A good example of this:
calculator. It’s basically an example of
a fixed-program computer. It does arithmetic. If you want play video games
on it, good luck. If you want to do word
processing on it, good luck. It’s designed to do
a specific thing. It’s a fixed-program computer. In fact, a lot of the other
really interesting early ones similarly have this flavor, to
give an example: I never know how to pronounce this,
Atanasoff, 1941. One of the earliest
computational things was a thing designed by a guy named
Atanasoff, and it basically solved linear equations. Handy thing to do if you’re
doing 1801, all right, or 1806, or whatever you want
to do those things in. All it could do, though, was
solve those equations. One of my favorite examples of
an early computer was done by Alan Turing, one of the great
computer scientists of all time, called the bombe, which
was designed to break codes. It was actually used during
WWII to break German Enigma codes. And what it was designed
to do, was to solve that specific problem. The point I’m trying to make is,
fixed-program computers is where we started, but it doesn’t
really get us to where we’d like to be. We want to capture this idea
of problem solving. So let’s see how
we’d get there. So even within this framework
of, given a description of a computation as a set of steps,
in the idea that I could build a circuit to do it, let me
suggest for you what would be a wonderful circuit to build. Suppose you could build a
circuit with the following property: the input to this
circuit would be any other circuit diagram. Give it a circuit diagram for
some computation, you give it to the circuit, and that circuit
would wonderfully reconfigure itself to act like
the circuits diagram. Which would mean, it could
act like a calculator. Or, it could act like
Turing’s bombe. Or, it could act like a
square root machine. So what would that circuit
look like? You can imagine these tiny
little robots wandering around, right? Pulling wires and pulling
out components and stacking them together. How would you build a circuit
that could take a circuit diagram in and make a machine
act like that circuit? Sounds like a neat challenge. Let me change the
game slightly. Suppose instead, I want a
machine that can take a recipe, the description of a
sequence of steps, take that as its input, and then that
machine will now act like what is described in that recipe. Reconfigure itself, emulate it,
however you want to use the words, it’s going to
change how it does the computation. That would be cool. And that exists. It’s called an interpreter. It is the basic heart
of every computer. What it is doing, is saying,
change the game. This is now an example of a
stored-program computer. What that means, in a
stored-program computer, is that I can provide to the
computer a sequence of instructions describing the
process I want it to execute. And inside of the machine, and
things we’ll talk about, there is a process that will allow
that sequence to be executed as described in that recipe,
so it can behave like any thing that I can describe
in one of those recipes. All right. That actually seems like a
really nice thing to have, and so let me show you what that
would basically look like. Inside of a stored-program
computer, we would have the following: we have a memory,
it’s connected to two things; control unit, in what’s called
an ALU, an arithmetic logic unit, and this can take in
input, and spit out output, and inside this stored-program
computer, excuse me, you have the following: you have a
sequence of instructions. And these all get
stored in there. Notice the difference. The recipe, the sequence of
instructions, is actually getting read in, and it’s
treated just like data. It’s inside the memory of the
machine, which means we have access to it, we can change it,
we can use it to build new pieces of code, as well as
we can interpret it. One other piece that goes
into this computer– I never remember where to put
the PC, John, control? ALU? Separate? I’ll put it separate–
you have a thing called a program counter. And here’s the basis
of the computation. That program counter points to
some location in memory, typically to the first
instruction in the sequence. And those instructions, by the
way, are very simple: they’re things like, take the value out
of two places in memory, and run them through the
multiplier in here, a little piece of circuitry, and
stick them back into someplace in memory. Or take this value out of
memory, run it through some other simple operation, stick
it back in memory. Having executed this
instruction, that counter goes up by one and we move
to the next one. We execute that instruction,
we move to the next one. Oh yeah, it looks a whole
lot like that. Some of those instructions will
involve tests: they’ll say, is something true? And if the test is true, it will
change the value of this program counter to point to
some other place in the memory, some other point in that
sequence of instructions, and you’ll keep processing. Eventually you’ll hopefully
stop, and a value gets spit out, and you’re done. That’s the heart
of a computer. Now that’s a slight
misstatement. The process to control it is
intriguing and interesting, but the heart of the computer is
simply this notion that we build our descriptions, our
recipes, on a sequence of primitive instructions. And then we have a
flow of control. And that flow of control is
what I just described. It’s moving through a sequence
of instructions, occasionally changing where we are
as we move around. OK. The thing I want you to take
away from this, then, is to think of this as, this is,
if you like, a recipe. And that’s really what
a program is. It’s a sequence of
instructions. Now, one of things I left
hanging is, I said, OK, you build it out of primitives. So one of the questions is,
well, what are the right primitives to use? And one of the things that was
useful here is, that we actually know that the set of
primitives that you want to use is very straight-forward. OK, but before I do that, let me
drive home this idea of why this is a recipe. Assuming I have a set of
primitive instructions that I can describe everything on, I
want to know what can I build. Well, I’m going to do the same
analogy to a real recipe. So, real recipe. I don’t know. Separate six eggs. Do something. Beat until the– sorry,
beat the whites until they’re stiff. Do something until an
end test is true. Take the yolks and mix them in
with the sugar and water– No. Sugar and flour I guess is
probably what I want, sugar and water is not going to do
anything interesting for me here– mix them into
something else. Do a sequence of things. A traditional recipe actually
is based on a small set of primitives, and a good chef
with, or good cook, I should say, with that set of
primitives, can create an unbounded number of
great dishes. Same thing holds true
in programming. Right. Given a fixed set of primitives,
all right, a good programmer can program
anything. And by that, I mean anything
that can be described in one of these process, you can
capture in that set of primitives. All right, the question is, as
I started to say, is, “What are the right primitives?” So
there’s a little bit of, a little piece of history
here, if you like. In 1936, that same guy, Alan
Turing, showed that with six simple primitives, anything that
could be described in a mechanical process, it’s
actually algorithmically, could be programmed just using
those six primitives. Think about that for a second. That’s an incredible
statement. It says, with six primitives,
I can rule the world. With six primitives, I
can program anything. A couple of really interesting
consequences of that, by the way, one of them is, it says,
anything you can do in one programming language,
you can do in another programming language. And there is no programming
language that is better– well actually, that’s not quite true,
there are some better at doing certain kinds of things–
but there’s nothing that you can do in C that
you can’t do in Fortran. It’s called Turing
compatibility. Anything you can do with one,
you can do with another, it’s based on that fundamental
result. OK. Now, fortunately we’re not going
to start with Turing’s six primitives, this would be
really painful programming, because they’re down at the
level of, “take this value and write it onto this tape.” First
of all, we don’t have tapes anymore in computers, and
even if we did, you don’t want to be programming
at that level. What we’re going to see with
programming language is that we’re going to use higher-level
abstracts. A broader set of primitives,
but nonetheless the same fundamental thing holds. With those six primitives,
you can do it. OK. So where are we here? What we’re saying is, in order
to do computation, we want to describe recipes, we want to
describe this sequence of steps built on some primitives,
and we want to describe the flow of control
that goes through those sequence of steps
as we carry on. So the last thing we need before
we can start talking about real programming
is, we need to describe those recipes. All right, And to describe
the recipes, we’re going to want a language. We need to know not only what
are the primitives, but how do we make things meaningful
in that language. Language. There we go. All right. Now, it turns out there are– I don’t know, John, hundreds? Thousands? Of programming languages? At least hundreds– of
programming languages around. PROFESSOR JOHN GUTTAG:
[UNINTELLIGIBLE] PROFESSOR ERIC GRIMSON: True. Thank you. You know, they all
have, you know, their pluses and minuses. I have to admit, in my career
here, I think I’ve taught in at least three languages, I
suspect you’ve taught more, five or six, John? Both of us have probably
programmed in more than those number of languages, at least
programmed that many, since we taught in those languages. One of the things you
want to realize is, there is no best language. At least I would argue that,
I think John would agree. We might both agree we have
our own nominees for worst language, there are
some of those. There is no best language. All right? They all are describing
different things. Having said that, some of them
are better suited for some things than others. Anybody here heard of MATLAB
Maybe programmed in MATLAB? It’s great for doing things with
vectors and matrices and things that are easily captured
in that framework. But there’s some things
that are a real pain to do in MATLAB. So MATLAB’s great for
that kind of thing. C is a great language for
programming things that control data networks,
for example. I happen to be, and John
teases me about this regularly, I’m an old-time Lisp
programmer, and that’s how I was trained. And I happen to like Lisp and
Scheme, it’s a great language when you’re trying to deal with
problems where you have arbitrarily structured
data sets. It’s particularly
good at that. So the point I want to make
here is that there’s no particularly best language. What we’re going to do is simply
use a language that helps us understand. So in this course, the
language we’re going to use is Python. Which is a pretty new language,
it’s growing in popularity, it has a lot of
the elements of some other languages because it’s more
recent, it inherits things from it’s pregenitors,
if you like. But one of the things I want to
stress is, this course is not about Python. Strange statement. You do need to know how to use
it, but it’s not about the details of, where do the
semi-colons go in Python. All right? It’s about using it to think. And what you should take away
from this course is having learned how to design recipes,
how to structure recipes, how to do things in modes
in Python. Those same tools
easily transfer to any other language. You can pick up another language
in a week, couple of weeks at most, once you
know how to do Python. OK. In order to talk about Python
and languages, I want to do one last thing to set the stage
for what we’re going to do here, and that’s to talk
about the different dimensions of a language. And there’re three I
want to deal with. The first one is, whether
this is a high-level or low-level language. That basically says,
how close are you the guts of the machine? A low-level language, we used
to call this assembly programming, you’re down at the
level of, your primitives are literally moving pieces of
data from one location of memory to another, through
a very simple operation. A high-level language, the
designer has created a much richer set of primitive
things. In a high-level language, square
root might simply be a primitive that you can use,
rather than you having to go over and code it. And there’re trade-offs
between both. Second dimension is, whether
this is a general versus a targeted language. And by that I mean, do the set
of primitives support a broad range of applications, or is
it really aimed at a very specific set of applications? I’d argue that MATLAB is
basically a targeted language, it’s targeted at matrices and
vectors and things like that. And the third one I want to
point out is, whether this is an interpreted versus
a compiled language. What that basically says
is the following: in an interpreted language, you take
what’s called the source code, the thing you write, it may go
through a simple checker but it basically goes to the
interpreter, that thing inside the machine that’s going to
control the flow of going through each one of
the instructions, and give you an output. So the interpreter is simply
operating directly on your code at run time. In a compiled language, you have
an intermediate step, in which you take the source code,
it runs through what’s called a checker or a compiler
or both, and it creates what’s called object code. And that does two things: one,
it helps catch bugs in your code, and secondly it often
converts it into a more efficient sequence of
instructions before you actually go off and run it. All right? And there’s trade-offs
between both. I mean, an interpreted language
is often easier to debug, because you can still see
your raw code there, but it’s not always as fast. A
compiled language is usually much faster in terms
of its execution. And it’s one of the things you
may want to trade off. Right. In the case of Python, it’s
a high-level language. I would argue, I think John
would agree with me, it’s basically a general-purpose
language. It happens to be better suited
for manipulating strings than numbers, for example,
but it’s really a general-purpose language. And it’s primarily– I shouldn’t say primarily, it
is an interpreted language. OK? As a consequence, it’s not as
good as helping debug, but it does let you– sorry, that’s the
wrong way of saying– it’s not as good at catching some
things before you run them, it is easier at some times
in debugging as you go along on the fly. OK. So what does Python look like? In order to talk about Python–
actually, I’m going to do it this way– we need
to talk about how to write things in Python. Again, you have to let me back
up slightly and set the stage. Our goal is to build recipes. You’re all going to be
great chefs by the time you’re done here. All right? Our goal is to take problems and
break them down into these computational steps, these
sequence of instructions that’ll allow us to capture
that process. To do that, we need to describe:
not only, what are the primitives, but how do we
capture things legally in that language, and interact
with the computer? And so for that, we
need a language. We’re about to start talking
about the elements of the language, but to do that, we
also need to separate out one last piece of distinction. Just like with a natural
language, we’re going to separate out syntax
versus semantics. So what’s syntax? Syntax basically says, what are
the legal expressions in this language? Boy, my handwriting is
atrocious, isn’t it? There’s a English sequence
of words. It’s not since syntactically
correct, right? It’s not a sentence. There’s no verb in there
anywhere, it’s just a sequence of nouns. Same thing in our languages. We have to describe how do you
put together legally formed expressions. OK? And as we add constructs to the
language, we’re going to talk about. Second thing we want to talk
about very briefly as we go along is the semantics
of the language. And here we’re going to break
out two pieces; static semantics and full semantics. Static semantics basically
says which programs are meaningful. Which expressions make sense. Here’s an English sentence. It’s syntactically correct. Right? Noun phrase, verb,
noun phrase. I’m not certain it’s meaningful,
unless you are in the habit of giving your
furniture personal names. What’s the point? Again, you can have things that
are syntactically legal but not semantically meaningful,
and static semantics is going to be a way
of helping us decide what expressions, what pieces of
code, actually have real meaning to it. All right? The last piece of it is, in
addition to having static semantics, we have sort
of full semantics. Which is, what does
the program mean? Or, said a different way,
what’s going to happen when I run it? That’s the meaning of
the expression. That’s what you want. All right? You want to know, what’s the
meaning of this piece of code? When I run it, what’s
going to happen? That’s what I want to build. The reason for pulling this out
is, what you’re going to see is, that in most languages,
and certainly in Python– we got lots of help
here– all right, Python comes built-in with something that
will check your static, sorry, your syntax for you. And in fact, as a sidebar, if
you turn in a problem set that is not syntactically correct,
there’s a simple button that you push that will check
your syntax. If you’ve turned in a program
that’s not syntactically correct, the TAs give
you a zero. Because it said you didn’t even
take the time to make sure the syntax is correct. The system will help
you find it. In Python, it’ll find it,
I think one bug at a time, right John? It finds one syntax error at
a time, so you have to be a little patient to do it,
but you can check that the syntax is right. You’re going to see that we
get some help here on the static semantics, and I’m going
to do an example in a second, meaning that the system,
some languages are better than others on it, but it
will try and help you catch some things that are not
semantically correct statically. In the case of Python, it does
that I think all at run time. I’m looking to you again,
John, I think there’s no pre-time checks. Its– sorry? PROFESSOR JOHN GUTTAG:
[UNINTELLIGIBLE] PROFESSOR ERIC GRIMSON:
There is some. OK. Most of them, I think though,
are primarily caught at run time, and that’s a little bit
of a pain because you don’t see it until you go and run the
code, and there are some, actually we’re going to see an
example I think in a second where you find it, but you
do get some help there. The problem is, things that you
catch here are actually the least worrisome bugs. They’re easy to spot, you can’t
run the program with them there, so you’re not going
to get weird answers. Not everything is going
to get caught in static semantics checking. Some things are going to
slide through, and that’s actually a bother. It’s a problem. Because it says, your program
will still give you a value, but it may not be what you
intended, and you can’t always tell, and that may propagate
it’s way down through a whole bunch of other computations
before it causes some catastrophic failure. So actually, the problem with
static semantics is you’d like it to catch everything, you
don’t always get it. Sadly we don’t get
much help here. Which is where we’d like it. But that’s part of your job. OK. What happens if you actually
have something that’s both syntactically correct, and
appears to have correct static semantics, and you run it? It could run and give you the
right answer, it could crash, it could loop forever, it could
run and apparently give you the right answer. And you’re not always going
to be able to tell. Well, you’ll know when it
crashes, that doesn’t help you very much, but you can’t
always tell whether something’s stuck in an infinite
loop or whether it’s simply taking a long
time to compute. You’d love to have a system that
spots that for you, but it’s not possible. And so to deal with
this last one, you need to develop style. All right? Meaning, we’re going to try to
help you with how to develop good programming style, but you
need to write in a way in which it is going to be easy
for you to spot the places that cause those semantic
bugs to occur. All right. If that sounds like a really
long preamble, it is. Let’s start with Python. But again, my goal here is to
let you see what computation’s about, why we need to do it,
I’m going to remind you one last time, our goal is to
be able to have a set of primitives that we combine
into complex expressions, which we can then abstract to
treat as primitives, and we want to use that sequence of
instructions in this flow of control computing, in order
to deduce new information. That imperative knowledge that
we talked about right there. So I’m going to start today,
we have about five or ten minutes left, I think, in
order– sorry, five minutes left– in order to do this
with some beginnings of Python, and we’re going to pick
this up obviously, next time, so; simple parts
of Python. In order to create any kinds of
expressions, we’re going to need values. Primitive data elements. And in Python, we have two to
start with; we have numbers, and we have strings. Numbers is what you’d expect. There’s a number. There’s another number. All right? Strings are captured in Python
with an open quote and some sequence of characters followed
by a closed quote. Associated with every data
type in Python is a type, which identifies the kind
of thing it is. Some of these are obvious. Strings are just a type
on their own. But for numbers, for example,
we can have a variety of types. So this is something that
we would call an integer, or an INT. And this is something
we would call a floating point, or a float. Or if you want to think of
it as a real number. And there’s some others
that we can see. We’re going to build up this
taxonomy if you like, but the reason it’s relevant is,
associated with each one of those types is a set of
operators that expect certain types of input in order
to do their job. And given those types of input,
will get back output. All right. In order to deal with this, let
me show you an example, and I hope that comes
up, great. What I have here is a Python
shell, and I’m going to just show you some simple examples
of how we start building expressions. And this’ll lead into what
you’re going to see next time as well as what you’re
going to do tomorrow. So. Starting with the shell, I
can type in expressions. Actually, let me back up
and do this in video. I can type in a number, I get
back a number, I can type in a string, I get back the string. Strings, by the way, can have
spaces in them, they can have other characters, it’s simply
a sequence of things, and notice, by the way, that the
string five– sorry, the string’s digit five digit
two is different than the number 52. The quotes are around them
to make that distinction. We’re going to see
why in a second. What I’m doing, by the way, here
is I’m simply typing in expressions to that
interpreter. It’s using its set of rules to
deduce the value and print them back out. Things I might like to do in
here is, I might like to do combinations of things
with these. So we have associated with
simple things, a set of operations. So for numbers, we have the
things you’d expect, the arithmetics. And let me show you some
examples of that. And actually, I’m going to do
one other distinction here. What I typed in, things like–
well, let me start this way– there’s an expression. And in Python the expression
is, operand, operator, operand, when we’re doing simple
expressions like this, and if I give it to the
interpreter, it gives me back exactly what you’d expect,
which is that value. OK? The distinction I’m going to
make is, that’s an expression. The interpreter is going
to get a value for it. When we start building
up code, we’re going to use commands. Or statements. Which are actually things that
take in a value and ask the computer to do something
with it. So I can similarly do this,
which is going to look strange because it’s going to give me
the same value back out, but it actually did a slightly
different thing. And notice, by the way, when I
typed it how print showed up in a different color? That’s the Python saying, that
is a command, that is a specific command to get the
value of the expression and print it back out. When we start writing code,
you’re going to see that difference, but for now, don’t
worry about it, I just want to plant that idea. OK. Once we’ve got that, we
can certainly, though, do things like this. Notice the quotes around it. And it treats it as a string,
it’s simply getting me back the value of that string,
52 times 7, rather than the value of it. Now, once we’ve got that, we
can start doing things. And I’m going to use print
here– if I could type, in order to just to get into that,
I can’t type, here we go– in order to get
into the habit. I can print out a string. I can print out– Ah!– Here’s a first example
of something that caught one of my things. This is a static
semantic error. So what went on here? I gave it an expression that
had an operand in there. It expected arithmetic types. But I gave two strings. And so it’s complaining at me,
saying, you can’t do this. I don’t know how to take
two strings and multiply them together. Unfortunately– now John you may
disagree with me on this one– unfortunately in Python
you can, however, do things like this. What do you figure that’s
going to do? Look legal? The string three times
the number three? Well it happens to give me
three threes in a row. I hate this. I’m sorry, John, I hate this. Because this is overloading that
multiplication operator with two different tasks. It’s saying, if you give
me two numbers, I’ll do the right thing. If you give me a number and
a string, I’m going to concatenate them together,
it’s really different operations, but nonetheless,
it’s what it’s going to do. STUDENT: [UNINTELLIGIBLE] PROFESSOR ERIC GRIMSON:
There you go. You know, there will be a
rebuttal phase a little later on, just like with the political
debates, and he likes it as a feature, I don’t
like it, you can tell he’s not a Lisp programmer and I am. All right. I want to do just a couple
more quick examples. Here’s another one. Ah-ha! Give you an example
of a syntax error. Because 52A doesn’t
make sense. And you might say, wait a
minute, isn’t that a string, and the answer’s no, I didn’t
say it’s a string by putting quotes around it. And notice how the machine
responds differently to it. In this case it says, this is
a syntax error, and it’s actually highlighting where
it came from so I can go back and fix it. All right. Let’s do a couple of other
simple examples. All right? I can do multiplication. I’ve already seen that. I can do addition. Three plus five. I can take something to a power,
double star, just take three to the fifth power. I can do division, right? Whoa. Right? Three divided by five is zero? Maybe in Bush econom– no, I’m
not going to do any political comments today, I will not
say that, all right? What happened? Well, this is one of
the places where you have to be careful. It’s doing integer division. So, three divided by five
is zero, with a remainder of three. So this is the correct answer. If I wanted to get full, real
division, I should make one of them a float. And yes, you can look at that
and say, well is that right? Well, up to some level of
accuracy, yeah, that’s .6 is what I’d like to get out. All right. I can do other things. In a particular, I have similar
operations on strings. OK, I can certainly print out
strings, but I can actually add strings together, and just
as you saw, I can multiply strings, you can kind of guess
what this is going to do. It is going to merge them
together into one thing. I want– I know I’m running you slightly
over, I want to do one last example, it’s, I also
want to be able to do, have variables to store things. And to do that, in this it says,
if I have a value, I want to keep it around,
to do that, I can do things like this. What does that statement do? It says, create a name for a
variable– which I just did there, in fact, let me type it
in– mystring, with an equal sign, which is saying, assign or
bind to that name the value of the following expression. As a consequence, I
can now refer to that just by its name. If I get the value of mystring,
there it is, or if I say, take mystring and add to it
the string, mylastname, and print it back out. So this is the first
start of this. What have we done? We’ve got values, numbers
and strings. We have operations to
associate with them. I just threw a couple up here. You’re going to get a chance
to explore them, and you’ll see not only are there the
standard numerics for strings, there are things like length
or plus or other things you can do with them. And once I have values, I want
to get a hold of them so I can give them names. And that’s what I just did
when I bound that. I said, use the name mystring
to be bound to or have the value of Eric, so I can refer
to it anywhere else that I want to use it. And I apologize for taking you
over, we’ll come back to this next time, please go to the
website to sign up for recitation for tomorrow.

100 thoughts on “Lec 1 | MIT 6.00 Introduction to Computer Science and Programming, Fall 2008”

  1. 0028: Eric Grimson
    EG: This is 6.00, also known as Introduction to Computer Science and Programming.
    ————————————————————————————————————————————–
    My name is Eric Grimson, I have together Professor John Guttag over here. We're going to be lecturing this term.

  2. 28:16
    fet_rev:
    EG: Some of those instructions will involve tests: they'll say, "Is sth true?" And if the test is true, it'll change the value of this Program Counter (PC) to point to some other place in the MEMORY …

  3. It´s one of the best proffesor that i have ever listen to.I would like that my programming professor would be like this man.

  4. I don't understand anything. This is the first time I have seen that lecture and English is not my native language but his accent is vivid. Thank you

  5. Revision Quiz

    1. What is the difference between declarative knowledge and imperative knowledge?

    2. What is the difference between fixed-program computer and stored-program computer?

    3. Describe the process of computation in stored-program computer (Draw the diagram).

    4. What is the difference between high-level and low-level language?

    5. What is the difference between general-purpose and targeted language?

    6. What is the difference between interpreted and compiled language?

    7. What is the pros and cons of an interpreted language?

    8. What is the difference between static and full semantics?

  6. You know what? For those Potato like students without any reaction or response, fine for me if I am THE PROF of the lecture. Cus I don't have any problems at all, but these kids have and plus they haven't full fledged to fly out side of the cage into the real mirage society. So WHO CARES AS LONG AS I HAVE GREAT TIME AND TRY MY BEST TO ENJOY MY LIFE. WHO CARES… HAHAHA

  7. 22:50 "if you want to play video games on [a calculator], good luck." little does he know people can literally download games onto certain texas instrument graphing calculators

  8. Why are people alway trying to be funny?
    Just do what you were paid to do. I'm here for the lecture, not your jokes. If I wanted to listen to jokes I'd go to a comedy club.

  9. lol any school system is stupid and corrupt. best learning is internet learning. the latter alone is not enough though, one also must have good learning skills to be a good learner.

  10. This professor probably never thought that 4 million people would end up watching him give his lecture that day.

  11. This just proves you cant buy an education. ( all the money in the world can’t buy your interest and engagement)

  12. ,…
    ok
    i suggest for all studentss according to me computer learning is not all about our intelligence it needs feelingss as well…
    good luck students for suchba great class wth great professors..

  13. Aaaaa… if we only have this type of education in Mexico… this would be a better place…

  14. 嗯,博士是电腦硬体驱动防骇系统指令,若个人有缐网络,骇客须入终端机而进入浮动線网中,而固定网大多企业而同样须从终端机再向固定网络或网站,,,而无線网只须电码数據即可,以及骇入晶片卫星定位以及手机防盗賣晶体和PiM槽晶体组或朋友群,,,SD,会型成占用机体空间垃圾讯息和暫存空间和,,,,,,,堆疊,使机体運仆,,,,,,,,,,SO,手机波須大量配对多而二处同行,所以骇客可能大慨也许会以空中侦测而,,,寻波吧??,鸽子猜想恐佈行动的份子,大致行为上,,,??。

  15. Well , this is a very old thesis , but seems we can answer and satisfy all parties by writing…writing is Research…so , by writing in french , in the text… " Parce que lorsque le Maître montre la Lune , le Disciple regarde le doigt."
    Sorry , Sir , Fellow , for this " Ingérence " from Heidelberg. Oui , il se trouve que je n'ai pas , présentement , de copies à corriger. Tell me my academic french is excellent. Auf Wiedersehen…

  16. I recently started studying programing at the age of 32 and I'm running into many obstacles already. The intro to this speech was really encouraging. Thank you!

  17. Intro to cs in my university was so bad that it made me rethink the class I was so passionate about. Now this. This just made 100% more sense. The professors are so much more knowledgeable from CS history, maths, and real applications. And I even enjoy these intimate albeit inappropriate jokes. Hahaha. Overall it’s nice that they aren’t trying to weed out people to drop the course.

  18. In India , there's only one full fledged course on AI and that is in Hyderabad where being from a specific religion , some goons will not let you live .

    I want to apply for MIT but I'm not rich to afford $10000 a year . Yes I'm late , I'm 17 , I can make a chatbot , but memory is not good for remembering chemical reactions 😔

    Now – I left developing chatbot 4 year ago , trying to get my hands to Irodov problem which are easier than organic chemistry

  19. Yea….. look where computer science has got us…….closer to global control. Here is the fact about education that no one is discussing. ALL advancements in ANY technology is being utilized in the pursuit of the agenda to form a one world government. ALL technological advancement has been used to CONTROL the citizens of the world is some fashion…..period…. end of discussion. Humanities number one goal is to remove the corrupt powers that control us and ……then……and only then…..pursue education for technological advancements. Until then….. everyone should boycott universities and their fucking astronomical unreasonable expenses to attend them. The world is out of fucking control and needs to be reassigned to powers that give a shit. The powers that be at this current date… do not give a shit about you…..they only care for themselves…..sad but fact.

  20. Hi i am from india i am going to enter college this august . I want to learn c language is this course enough ?

  21. I feel kinda embarrassed to be from the generation this crowd represents, it's kinda rude to be so passive.

  22. The crazy part of a small YouTube can feel is daily you wake up in the morning and sleep dreaming about getting more subs and nothing has changed on your channel😂 lool

  23. People who are commenting saying that the class isn't very engaging, you fail to consider that the sophomore undergrads doing course 6 get 6.3 hours of sleep on average. That coupled with the intense coursework results in a certain lack of social awareness.
    http://tech.mit.edu/V132/N59/pressure/sleepinghours/index.htm

  24. Great!!! But it's impossible to read the Code in that color.

    I Hope that change un the furure. I'm looking foward this classes

  25. In the first f**king 13 minutes he's just talking about the website and the students problem! I mean what the hack?????

  26. wow this is wonderful this guy explains thing so clearly its amazing that MIT put this up even if it is from 2008

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