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CNET Japan http://japan.cnet.com/
スマートフォン版へ
スマートフォン版へ CNET is available in the following editions: お使いのブラウザは最新版ではありません。最新のブラウザでご覧ください。 UPDATE楽天の子会社、Rakuten Koboが7.6インチの大画面を搭載した電子書籍リーダーの新製品「Kobo Aura ONE」を発表した。前モデルと比較して... 2016/08/18 11:17 2016/08/18 11:30 2016/08/18 08:00 2016/08/18 14:4 ...
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María Jesús Lamarca Lapuente http://hipertexto.info/lamarcalapuente.htm
María Jesús Lamarca Lapuente. Hipertexto: El
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Python - Wikidata https://www.wikidata.org/wiki/Q28865
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Python (Q28865) edit edit edit Python general-purpose, high-level programming language Statements Python 3.5.1 and 3.4.4rc1 are now available (English) Python 2.7.11 (English) Identifiers Sitelinks (85 entries) edit edit edit
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Python - Wikidata https://www.wikidata.org/wiki/Q28865
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Python (Q28865) edit edit edit Python general-purpose, high-level programming language Statements Python 3.5.1 and 3.4.4rc1 are now available (English) Python 2.7.11 (English) Identifiers Sitelinks (85 entries) edit edit edit
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CIENCIAS DE LA COMPUTACION | DATASENA https://datasena.wordpress.com/about/
DATASENA
Definición 1: Las ciencias de la computación son aquellas que abarcan el estudio de las bases teóricas de la información y la computación, así como su aplicación en sistemas computacionales.[1] [2] [3] Existen diversos campos o disciplinas dentr ...
0:00 The following content is provided under a Creative 0:02 Commons license. 0:03 Your support will help MIT OpenCourseware continue to 0:06 offer high-quality educational resources for free. 0:10 To make a donation, or view additional materials from 0:13 hundreds of MIT courses, visit MIT OpenCourseware, at 0:17 ocw.mit.edu . 0:17 PROFESSOR: Good morning. 0:18 Try it again. 0:19 Good morning. 0:22 STUDENTS: Good morning. 0:25 PROFESSOR: Thank you. 0:27 This is 6.00, also known as Introduction to Computer 0:31 Science and Programming. 0:32 My name is Eric Grimson, I have together Professor John 0:35 Guttag over here, we're going to be lecturing 0:37 the course this term. 0:39 I want to give you a heads up; you're getting some serious 0:41 firepower this term. 0:43 John was department head for ten years, felt like a 0:47 century, and in course six, I'm the current department 0:50 head in course six. 0:51 John's been lecturing for thirty years, roughly. 0:55 All right, I'm the young guy, I've only been lecturing for 0:58 twenty-five years. 0:59 You can tell, I have less grey hair than he does. 1:03 What I'm trying to say to you is, we take this 1:05 course really seriously. 1:07 We hope you do as well. 1:08 But we think it's really important for the department 1:10 to help everybody learn about computation, and that's what 1:14 this course is about. 1:16 What I want to do today is three things: I'm going to 1:19 start-- actually, I shouldn't say start, I'm going to do a 1:22 little bit of administrivia, the kinds of things you need 1:24 to know about how we're going to run the course. 1:26 I want to talk about the goal of the course, what it is 1:30 you'll be able to do at the end of this course when you 1:32 get through it, and then I want to begin talking about 1:35 the concepts and tools of computational thinking, which 1:39 is what we're primarily going to focus on here. 1:41 We're going to try and help you learn how to think like a 1:43 computer scientist, and we're going to begin talking about 1:45 that towards the end of this lecture and of course 1:47 throughout the rest of the lectures that carry on. 1:50 Right, let's start with the goals. 1:52 I'm going to give you goals in two levels. 1:55 The strategic goals are the following: we want to help 1:58 prepare freshmen and sophomores who are interested 2:02 in majoring in course six to get an easy entry into the 2:05 department, especially for those students who don't have 2:07 a lot of prior programming experience. 2:09 If you're in that category, don't panic, you're 2:11 going to get it. 2:12 We're going to help you ramp in and you'll certainly be 2:14 able to start the course six curriculum and do just fine 2:17 and still finish on target. 2:20 We don't expect everybody to be a course six major, 2:22 contrary to popular opinion, so for those are you not in 2:25 that category, the second thing we want to do is we want 2:27 to help students who don't plan to major in course six to 2:30 feel justifiably confident in their ability to write and 2:34 read small pieces of code. 2:37 For all students, what we want to do is we want to give you 2:40 an understanding of the role computation can and cannot 2:43 play in tackling technical problems. So that you will 2:47 come away with a sense of what you can do, what you can't do, 2:50 and what kinds of things you should use to tackle complex 2:53 problems. 2:54 And finally, we want to position all students so that 2:56 you can easily, if you like, compete for things like your 3:00 office and summer jobs. 3:02 Because you'll have an appropriate level of 3:04 confidence and competence in your ability to do 3:06 computational problem solving. 3:08 Those are the strategic goals. 3:10 Now, this course is primarily aimed at students who have 3:15 little or no prior programming experience. 3:19 As a consequence, we believe that no student here is 3:21 under-qualified for this course: you're all MIT 3:24 students, you're all qualified to be here. 3:26 But we also hope that there aren't any students here who 3:29 are over-qualified for this course. 3:31 And what do I mean by that? 3:32 If you've done a lot prior programming, this is probably 3:37 not the best course for you, and if you're in that 3:39 category, I would please encourage you to talk to John 3:42 or I after class about what your goals are, what kind of 3:45 experience you have, and how we might find you a course 3:48 that better meets your goals. 3:51 Second reason we don't want over-qualified students in the 3:54 class, it sounds a little nasty, but the second reason 3:56 is, an over-qualified student, somebody who's, I don't know, 3:59 programmed for Google for the last five years, is going to 4:03 have an easy time in this course, but we don't want such 4:05 a student accidentally intimidating the rest of you. 4:08 We don't want you to feel inadequate when you're simply 4:12 inexperienced. 4:13 And so, it really is a course aimed at students with little 4:16 or no prior programming experience. 4:18 And again, if you're not in that category, talk to John or 4:20 I after class, and we'll help you figure out where you might 4:22 want to go. 4:24 OK. 4:24 Those are the top-level goals of the course. 4:26 Let's talk sort of at a more tactical level, about what do 4:29 we want you to know in this course. 4:31 What we want you to be able to do by the time 4:33 you leave this course? 4:34 So here are the skills that we would like you to acquire. 4:41 Right, the first skill we want you to acquire, is we want you 4:44 to be able to use the basic tools of computational 4:46 thinking to write small scale programs. I'm going to keep 4:50 coming back to that idea, but I'm going to call it 4:52 computational thinking. 4:57 And that's so you can write small pieces of code. 5:00 And small is not derogatory here, by the way, it just says 5:02 the size of things you're going to be able to do. 5:05 Second skill we want you to have at the end of this course 5:08 is the ability to use a vocabulary of computational 5:10 tools in order to be able to understand 5:13 programs written by others. 5:15 So you're going to be able to write, you're going 5:16 to be able to read. 5:19 This latter skill, by the way, is incredibly valuable. 5:24 Because you won't want to do everything from scratch 5:26 yourself, you want to be able to look at what is being 5:28 created by somebody else and understand what is inside of 5:31 there, whether it works correctly and how you can 5:33 build on it. 5:34 This is one of the few places where 5:35 plagiarism is an OK thing. 5:37 It's not bad to, if you like, learn from the skills of 5:40 others in order to create something you want to write. 5:42 Although we'll come back to plagiarism as a 5:44 bad thing later on. 5:46 Third thing we want you to do, is to understand the 5:48 fundamental both capabilities and limitations of 5:52 computations, and the costs associated with them. 5:55 And that latter statement sounds funny, you don't think 5:57 of computations having limits, but they do. 5:59 There're some things that cannot be computed. 6:01 We want you to understand where those limits are. 6:03 So you're going to be able to understand 6:05 abilities and limits. 6:15 And then, finally, the last tactical skill that you're 6:18 going to get out of this course is you're going to have 6:19 the ability to map scientific problems into a 6:22 computational frame. 6:24 So you're going to be able to take a description of a 6:26 problem and map it into something computational. 6:37 Now if you think about it, boy, it sounds 6:39 like grammar school. 6:41 We're going to teach you to read, we're going to teach you 6:43 to write, we're going to teach you to understand what you can 6:46 and cannot do, and most importantly, we're going to 6:49 try and give you the start of an ability to take a 6:52 description of a problem from some other domain, and figure 6:55 out how to map it into that domain of computation so you 6:57 can do the reading and writing that you want to do. 7:01 OK, in a few minutes we're going to start talking then 7:03 about what is computation, how are we going to start building 7:05 those tools, but that's what you should take away, that's 7:07 what you're going to gain out of this course by the time 7:09 you're done. 7:11 Now, let me take a sidebar for about five minutes to talk 7:14 about course administration, the administrivia, things that 7:17 we're going to do in the course, just so you know what 7:19 the rules are. 7:20 Right, so, class is two hours of lecture a week. 7:24 You obviously know where and you know when, 7:26 because you're here. 7:27 Tuesdays and Thursdays at 11:00. 7:29 One hour of recitation a week, on Fridays, and we'll come 7:32 back in a second to how you're going to get set up for that. 7:34 And nine hours a week of outside-the-class work. 7:38 Those nine hours are going to be primarily working on 7:40 problem sets, and all the problems sets are going to 7:42 involve programming in Python, which is the language we're 7:45 going to be using this term. 7:48 Now, one of the things you're going to see is the first 7:50 problem sets are pretty easy. 7:51 Actually, that's probably wrong, John, right? 7:52 They're very easy. 7:54 And we're going to ramp up. 7:55 By the time you get to the end of the term, you're going to 7:57 be dealing with some fairly complex things, so one of the 7:59 things you're going to see is, we're going to make heavy use 8:01 of libraries, or code written by others. 8:04 It'll allow you to tackle interesting problems I'll have 8:06 you to write from scratch, but it does mean that this skill 8:11 here is going to be really valuable. 8:13 You need to be able to read that code and understand it, 8:15 as well as write your own. 8:18 OK. 8:19 Two quizzes. 8:20 During the term, the dates have already been scheduled. 8:23 John, I forgot to look them up, I think it's October 2nd 8:25 and November 4th, it'll be on the course website. 8:29 My point is, go check the course website, which by the 8:31 way is right there. 8:34 If you have, if you know you have a conflict with one of 8:37 those quiz dates now, please see John or I right away. 8:40 We'll arrange something ahead of time. 8:42 But if you-- 8:44 The reason I'm saying that is, you know, you know that you're 8:45 getting married that day for example, we will excuse you 8:47 from the quiz to get married. 8:49 We'll expect you come right back to do the quiz by the 8:51 way, but the-- 8:53 Boy, tough crowd. 8:54 All right. 8:57 If you have a conflict, please let us know. 8:59 Second thing is, if you have an MIT documented special need 9:03 for taking quizzes, please see John or I well in advance. 9:07 At least two weeks before the quiz. 9:08 Again, we'll arrange for this, but you need to give us enough 9:10 warning so that we can deal with that. 9:13 OK, the quizzes are open book. 9:16 This course is not about memory. 9:20 It's not how well you can memorize facts: in fact, I 9:22 think both John and I are a little sensitive to memory 9:24 tests, given our age, right John? 9:26 This is not about how you memorize things, it's about 9:28 how you think. 9:29 So they're open note, open book. 9:30 It's really going to test your ability to think. 9:34 The grades for the course will be assigned roughly, and I use 9:38 the word roughly because we reserve the right to move 9:40 these numbers around a little bit, but basically in the 9:42 following percentages: 55% of your grade comes from the 9:44 problem sets, the other 45% come from the quizzes. 9:48 And I should've said there's two quizzes and a final exam. 9:50 I forgot, that final exam during final period. 9:52 So the quiz percentages are 10%, 15%, and 20%. 9:55 Which makes up the other 45%. 9:59 OK. 10:00 Other administrivia. 10:02 Let me just look through my list here. 10:05 First problem set, problem set zero, has already been posted. 10:07 This is a really easy one. 10:09 We intend it to be a really easy problem set. 10:11 It's basically to get you to load up Python on your machine 10:14 and make sure you understand how to interact with it. 10:17 The first problem set will be posted shortly, it's also 10:19 pretty boring-- somewhat like my lectures but not John's-- 10:23 and that means, you know, we want you just to 10:25 get going on things. 10:26 Don't worry, we're going to make them more interesting as 10:27 you go along. 10:28 Nonetheless, I want to stress that none of these problems 10:31 sets are intended to be lethal. 10:33 We're not using them to weed you out, we're using them to 10:36 help you learn. 10:36 So if you run into a problem set that just, you 10:39 don't get, all right? 10:41 Seek help. 10:43 Could be psychiatric help, could be a TA. 10:46 I recommend the TA. 10:47 My point being, please come and talk to somebody. 10:50 The problems are set up so that, if you start down the 10:53 right path, it should be pretty straight-forward to 10:55 work it through. 10:56 If you start down a plausible but incorrect path, you can 11:00 sometimes find yourself stuck in the weeds somewhere, and we 11:02 want to bring you back in. 11:03 So part of the goal here is, this should not be a grueling, 11:08 exhausting kind of task, it's really something that should 11:10 be helping you learn the material. 11:12 If you need help, ask John, myself, or the TAs. 11:15 That's what we're here for. 11:17 OK. 11:18 We're going to run primarily a paperless subject, that's why 11:22 the website is there. 11:23 Please check it, that's where everything's going to be 11:24 posted in terms of things you need to know. 11:27 In particular, please go to it today, you will find a form 11:30 there that you need to fill out to register for, or sign 11:33 up for rather, a recitation. 11:35 Recitations are on Friday. 11:37 Right now, we have them scheduled at 9:00, 10:00, 11:39 11:00, 12:00, 1:00, and 2:00. 11:41 We may drop one of the recitations, just depending on 11:45 course size, all right? 11:46 So we reserve the right, unfortunately, to have to move 11:48 you around. 11:49 My guess is that 9:00 is not going to be a tremendously 11:52 popular time, but maybe you'll surprise me. 11:54 Nonetheless, please go in and sign up. 11:56 We will let you sign up for whichever recitation makes 11:58 sense for you. 11:59 Again, we reserve the right to move people around if we have 12:02 to, just to balance load, but we want you to find something 12:04 that fits your schedule rather than ours. 12:08 OK. 12:09 Other things. 12:10 There is no required text. 12:12 If you feel exposed without a text book, you really have to 12:17 have a textbook, you'll find one recommended-- actually I'm 12:20 going to reuse that word, John, at least suggest it, on 12:23 the course website. 12:24 I don't think either of us are thrilled with the text, it's 12:26 the best we've probably found for Python, it's OK. 12:28 If you need it, it's there. 12:29 But we're going to basically not rely on any specific text. 12:33 Right. 12:34 Related to that: attendance here is 12:36 obviously not mandatory. 12:38 You ain't in high school anymore. 12:40 I think both of us would love to see your smiling faces, or 12:42 at least your faces, even if you're not 12:44 smiling at us every day. 12:46 Point I want to make about this, though, is that we are 12:49 going to cover a lot of material that is not in the 12:52 assigned readings, and we do have assigned readings 12:53 associated with each one of these lectures. 12:57 If you choose not to show up today-- or sorry, you did 13:00 choose to show up today, if you choose not to show up in 13:03 future days-- we'll understand, but please also 13:05 understand that the TAs won't have a lot of patience with 13:08 you if you're asking a question about something that 13:10 was either covered in the readings, or covered in the 13:12 lecture and is pretty straight forward. 13:14 All right? 13:14 We expect you to behave responsibly 13:16 and we will as well. 13:18 All right. 13:20 I think the last thing I want to say is, we will not be 13:22 handing out class notes. 13:26 Now this sounds like a draconian measure; 13:27 let me tell you why. 13:29 Every study I know of, and I suspect every one John knows, 13:31 about learning, stresses that students learn best when they 13:35 take notes. 13:36 Ironically, even if they never look at them. 13:40 OK. 13:40 The process of writing is exercising both halves of your 13:44 brain, and it's actually helping you learn, and so 13:46 taking notes is really valuable thing. 13:48 Therefore we're not going to distribute notes. 13:50 What we will distribute for most lectures is a handout 13:53 that's mostly code examples that we're going to do. 13:55 I don't happen to have one today because we're not going 13:57 to do a lot of code. 13:58 We will in future. 13:59 Those notes are going to make no sense, I'm guessing, 14:02 outside of the lecture, all right? 14:04 So it's not just, you can swing by 11:04 and grab a copy 14:08 and go off and catch some more sleep. 14:10 What we recommend is you use those notes to take your own 14:13 annotations to help you understand what's going on, 14:15 but we're not going to provide class notes. 14:17 We want you to take your own notes to help you, if you 14:20 like, spur your own learning process. 14:23 All right. 14:24 And then finally, I want to stress that John, myself, all 14:28 of the staff, our job is to help you learn. 14:32 That's what we're here for. 14:32 It's what we get excited about. 14:35 If you're stuck, if you're struggling, if you're not 14:38 certain about something, please ask. 14:40 We're not mind readers, we can't tell when you're 14:42 struggling, other than sort of seeing the expression on your 14:44 face, we need your help in identifying that. 14:48 But all of the TAs, many of whom are sitting down in the 14:50 front row over here, are here to help, so come and ask. 14:53 At the same time, remember that they're students too. 14:56 And if you come and ask a question that you could have 14:59 easily answered by doing the reading, coming to lecture, or 15:02 using Google, they're going to have less patience. 15:05 But helping you understand things that really are a 15:07 conceptual difficulty is what they're here for and what 15:10 we're here for, so please come and talk to us. 15:14 OK. 15:15 That takes care of the administrivia preamble. 15:17 John, things we add? 15:18 PROFESSOR GUTTAG: Two more quick things. 15:34 This semester, your class is being videotaped for 15:35 OpenCourseware. 15:35 If any of you don't want your image recorded and posted on 15:36 the web, you're supposed to sit in the back three rows. 15:38 PROFESSOR GRIMSON: Ah, thank you. 15:39 I forgot. 15:39 PROFESSOR GUTTAG: --Because the camera may pan. 15:40 I think you're all very good-looking and give MIT a 15:40 good image, so please, feel free to be filmed. 15:40 PROFESSOR GRIMSON: I'll turn around, so if you want to, you 15:45 know, move to the back, I won't see who moves. 15:48 Right. 15:48 Great. 15:48 Thank you, John. 15:49 PROFESSOR GUTTAG: So that, the other thing I want to mention 15:57 is, recitations are also very important. 16:00 We will be covering material in recitations that're not in 16:00 the lectures, not in the reading, and we do expect you 16:03 to attend recitations. 16:03 PROFESSOR GRIMSON: Great. 16:04 Thanks, John. 16:06 Any questions about the administrivia? 16:08 I know it's boring, but we need to do it so you know what 16:10 the ground rules are. 16:12 Good. 16:13 OK. 16:14 Let's talk about computation. 16:16 As I said, our strategic goal, our tactical goals, are to 16:19 help you think like a computer scientist. Another way of 16:23 saying it is, we want to give you the skill so that you can 16:25 make the computer do what you want it to do. 16:28 And we hope that at the end of the class, every time you're 16:30 confronted with some technical problem, one of your first 16:32 instincts is going to be, "How do I write the piece of code 16:35 that's going to help me solve that?" 16:37 So we want to help you think like a computer 16:39 scientist. All right. 16:41 And that, is an interesting statement. 16:45 What does it mean, to think like a computer scientist? 16:55 Well, let's see. 16:59 The primary knowledge you're going to take away from this 17:00 course is this notion of computational problem solving, 17:02 this ability to think in 17:04 computational modes of thought. 17:07 And unlike in a lot of introductory courses, as a 17:10 consequence, having the ability to memorize is not 17:12 going to help you. 17:13 It's really learning those notions of the tools that you 17:16 want to use. 17:18 What in the world does it mean to say 17:19 computational mode of thought? 17:20 It sounds like a hifalutin phrase you use when you're 17:22 trying to persuade a VC to fund you. 17:24 Right. 17:25 So to answer this, we really have to ask a different 17:27 question, a related question; so, what's computation? 17:31 It's like a strange statement, right? 17:32 What is computation? 17:35 And part of the reason for putting it up is that I want 17:38 to, as much as possible, answer that question by 17:41 separating out the mechanism, which is the computer, from 17:45 computational thinking. 17:47 Right. 17:47 The artifact should not be what's driving this. 17:49 It should be the notion of, "What does it mean to do 17:51 computation?" 17:53 Now, to answer that, I'm going to back up one more level. 17:56 And I'm going to pose what sounds like a philosophy 17:57 question, which is, "What is knowledge?" And you'll see in 18:01 about two minutes why I'm going to do this. 18:02 But I'm going to suggest that I can divide knowledge into at 18:04 least two categories. 18:07 OK, and what is knowledge? 18:08 And the two categories I'm going to divide them into are 18:12 declarative and imperative knowledge. 18:19 Right. 18:20 What in the world is declarative knowledge? 18:22 Think of it as statements of fact. 18:25 It's assertions of truth. 18:27 Boy, in this political season, that's a really dangerous 18:29 phrase to use, right? 18:30 But it's a statement of fact. 18:32 I'll stay away from the political comments. 18:34 Let me give you an example of this. 18:35 Right. 18:36 Here's a declarative statement. 18:37 The square root of x is that y such that y squared equals x, 18:46 y's positive. 18:48 You all know that. 18:50 But what I want you to see here, is that's a 18:52 statement of fact. 18:54 It's a definition. 18:55 It's an axiom. 18:55 It doesn't help you find square roots. 19:00 If I say x is 2, I want to know, what's the square root 19:02 of 2, well if you're enough of a geek, you'll say 1.41529 or 19:06 whatever the heck it is, but in general, this doesn't help 19:10 you find the square root. 19:12 The closest it does is it would let you test. You know, 19:15 if you're wandering through Harvard Square and you see an 19:17 out-of-work Harvard grad, they're handing out examples 19:19 of square roots, they'll give you an example and you can 19:21 test it to see, is the square root of 19:23 2, 1.41529 or whatever. 19:26 I don't even get laughs at Harvard jokes, John, I'm going 19:29 to stop in a second here, all right? 19:31 All right, so what am I trying to say here? 19:33 It doesn't -- yeah, exactly. 19:36 We're staying away from that, really quickly, especially 19:38 with the cameras rolling. 19:39 All right. 19:40 What am I trying to say? 19:41 It tells you how you might test something but it doesn't 19:44 tell you how to. 19:46 And that's what imperative knowledge is. 19:48 Imperative knowledge is a description of 19:51 how to deduce something. 19:52 So let me give you an example of a piece 19:54 of imperative knowledge. 19:56 All right, this is actually a very old piece of imperative 19:58 knowledge for computing square roots, it's attributed to 20:00 Heron of Alexandria, although I believe that the Babylonians 20:04 are suspected of knowing it beforehand. 20:07 But here is a piece of imperative knowledge. 20:09 All right? 20:10 I'm going to start with a guess, I'm going to call it g. 20:17 And then I'm going to say, if g squared is close to x, stop. 20:26 And return g. 20:28 It's a good enough answer. 20:30 Otherwise, I'm going to get a new guess by taking g, x over 20:38 g, adding them, and dividing by two. 20:42 Then you take the average of g and x over g. 20:44 Don't worry about how came about, Heron found this out. 20:47 But that gives me a new guess, and I'm going to repeat. 20:56 That's a recipe. 20:58 That's a description of a set of steps. 21:01 Notice what it has, it has a bunch of nice things that we 21:04 want to use, right? 21:05 It's a sequence of specific instructions 21:08 that I do in order. 21:10 Along the way I have some tests, and depending on the 21:13 value of that test, I may change where I am in that 21:17 sequence of instructions. 21:18 And it has an end test, something that tells me when 21:20 I'm done and what the answer is. 21:22 This tells you how to find square roots. 21:24 it's how-to knowledge. 21:25 It's imperative knowledge. 21:27 All right. 21:27 That's what computation basically is about. 21:31 We want to have ways of capturing this process. 21:35 OK, and that leads now to an interesting question, which 21:37 would be, "How do I build a mechanical process to capture 21:43 that set of computations?" So I'm going to suggest that 21:46 there's an easy way to do it-- 21:50 I realized I did the boards in the wrong order here-- one of 21:53 the ways I could do it is, you could imagine building a 21:55 little circuit to do this. 21:57 If I had a couple of elements of stored values in it, I had 22:00 some wires to move things around, I had a little thing 22:02 to do addition, little thing to do division, and a 22:05 something to do the testing, I could build a little circuit 22:07 that would actually do this computation. 22:09 OK. 22:11 That, strange as it sounds, is actually an example of the 22:15 earliest computers, because the earliest computers were 22:18 what we call fixed-program computers, meaning that they 22:31 had a piece of circuitry designed to do a specific 22:34 computation. 22:35 And that's what they would do: they would do that specific 22:38 computation. 22:40 You've seen these a lot, right? 22:41 A good example of this: calculator. 22:47 It's basically an example of a fixed-program computer. 22:51 It does arithmetic. 22:53 If you want play video games on it, good luck. 22:55 If you want to do word processing on it, good luck. 22:58 It's designed to do a specific thing. 23:00 It's a fixed-program computer. 23:03 In fact, a lot of the other really interesting early ones 23:05 similarly have this flavor, to give an example: I never know 23:09 how to pronounce this, Atanasoff, 1941. 23:14 One of the earliest computational things was a 23:16 thing designed by a guy named Atanasoff, and it basically 23:18 solved linear equations. 23:22 Handy thing to do if you're doing 1801, all right, or 23:26 1806, or whatever you want to do those things in. 23:29 All it could do, though, was solve those equations. 23:31 One of my favorite examples of an early computer was done by 23:36 Alan Turing, one of the great computer scientists of all 23:39 time, called the bombe, which was designed to break codes. 23:43 It was actually used during WWII to break 23:45 German Enigma codes. 23:46 And what it was designed to do, was to solve 23:48 that specific problem. 23:49 The point I'm trying to make is, fixed-program computers is 23:53 where we started, but it doesn't really get us to where 23:55 we'd like to be. 23:55 We want to capture this idea of problem solving. 23:58 So let's see how we'd get there. 24:01 So even within this framework of, given a description of a 24:05 computation as a set of steps, in the idea that I could build 24:08 a circuit to do it, let me suggest for you what would be 24:10 a wonderful circuit to build. 24:13 Suppose you could build a circuit with the following 24:15 property: the input to this circuit would be any other 24:18 circuit diagram. 24:20 Give it a circuit diagram for some computation, you give it 24:22 to the circuit, and that circuit would wonderfully 24:26 reconfigure itself to act like the circuits diagram. 24:30 Which would mean, it could act like a calculator. 24:33 Or, it could act like Turing's bombe. 24:35 Or, it could act like a square root machine. 24:38 So what would that circuit look like? 24:39 You can imagine these tiny little robots wandering 24:42 around, right? 24:42 Pulling wires and pulling out components and 24:44 stacking them together. 24:45 How would you build a circuit that could take a circuit 24:47 diagram in and make a machine act like that circuit? 24:53 Sounds like a neat challenge. 24:55 Let me change the game slightly. 24:59 Suppose instead, I want a machine that can take a 25:02 recipe, the description of a sequence of steps, take that 25:07 as its input, and then that machine will now act like what 25:12 is described in that recipe. 25:15 Reconfigure itself, emulate it, however you want to use 25:17 the words, it's going to change how it does the 25:19 computation. 25:21 That would be cool. 25:23 And that exists. 25:24 It's called an interpreter. 25:26 It is the basic heart of every computer. 25:29 What it is doing, is saying, change the game. 25:33 This is now an example of a stored-program computer. 25:40 What that means, in a stored-program computer, is 25:48 that I can provide to the computer a sequence of 25:51 instructions describing the process I want it to execute. 25:55 And inside of the machine, and things we'll talk about, there 25:58 is a process that will allow that sequence to be executed 26:02 as described in that recipe, so it can behave like any 26:06 thing that I can describe in one of those recipes. 26:09 All right. 26:10 That actually seems like a really nice thing to have, and 26:14 so let me show you what that would basically look like. 26:19 Inside of a stored-program computer, we would have the 26:22 following: we have a memory, it's connected to two things; 26:31 control unit, in what's called an ALU, an arithmetic logic 26:37 unit, and this can take in input, and spit out output, 26:46 and inside this stored-program computer, excuse me, you have 26:50 the following: you have a sequence of instructions. 26:55 And these all get stored in there. 27:03 Notice the difference. 27:05 The recipe, the sequence of instructions, is actually 27:07 getting read in, and it's treated just like data. 27:10 It's inside the memory of the machine, which means we have 27:12 access to it, we can change it, we can use it to build new 27:15 pieces of code, as well as we can interpret it. 27:19 One other piece that goes into this computer-- 27:21 I never remember where to put the PC, John, control? 27:23 ALU? 27:25 Separate? 27:26 I'll put it separate-- you have a thing 27:29 called a program counter. 27:31 And here's the basis of the computation. 27:34 That program counter points to some location in memory, 27:38 typically to the first instruction in the sequence. 27:43 And those instructions, by the way, are very simple: they're 27:45 things like, take the value out of two places in memory, 27:48 and run them through the multiplier in here, a little 27:51 piece of circuitry, and stick them back into 27:53 someplace in memory. 27:54 Or take this value out of memory, run it through some 27:57 other simple operation, stick it back in memory. 28:00 Having executed this instruction, that counter goes 28:03 up by one and we move to the next one. 28:05 We execute that instruction, we move to the next one. 28:08 Oh yeah, it looks a whole lot like that. 28:13 Some of those instructions will involve tests: they'll 28:16 say, is something true? 28:18 And if the test is true, it will change the value of this 28:22 program counter to point to some other place in the 28:25 memory, some other point in that sequence of instructions, 28:28 and you'll keep processing. 28:30 Eventually you'll hopefully stop, and a value gets spit 28:32 out, and you're done. 28:34 That's the heart of a computer. 28:35 Now that's a slight misstatement. 28:37 The process to control it is intriguing and interesting, 28:39 but the heart of the computer is simply this notion that we 28:42 build our descriptions, our recipes, on a sequence of 28:46 primitive instructions. 28:47 And then we have a flow of control. 28:50 And that flow of control is what I just described. 28:51 It's moving through a sequence of instructions, occasionally 28:53 changing where we are as we move around. 28:57 OK. 28:58 The thing I want you to take away from this, then, is to 29:02 think of this as, this is, if you like, a recipe. 29:06 And that's really what a program is. 29:19 It's a sequence of instructions. 29:21 Now, one of things I left hanging is, I said, OK, you 29:23 build it out of primitives. 29:24 So one of the questions is, well, what are the right 29:25 primitives to use? 29:28 And one of the things that was useful here is, that we 29:31 actually know that the set of primitives that you want to 29:33 use is very straight-forward. 29:37 OK, but before I do that, let me drive home this idea of why 29:39 this is a recipe. 29:42 Assuming I have a set of primitive instructions that I 29:44 can describe everything on, I want to know what can I build. 29:47 Well, I'm going to do the same analogy to a real recipe. 29:49 So, real recipe. 29:51 I don't know. 29:51 Separate six eggs. 29:54 Do something. 29:55 Beat until the-- sorry, beat the whites 29:57 until they're stiff. 29:59 Do something until an end test is true. 30:02 Take the yolks and mix them in with the sugar and water-- 30:04 No. 30:05 Sugar and flour I guess is probably what I want, sugar 30:06 and water is not going to do anything interesting for me 30:08 here-- mix them into something else. 30:11 Do a sequence of things. 30:13 A traditional recipe actually is based on a small set of 30:17 primitives, and a good chef with, or good cook, I should 30:21 say, with that set of primitives, can create an 30:23 unbounded number of great dishes. 30:26 Same thing holds true in programming. 30:28 Right. 30:29 Given a fixed set of primitives, all right, a good 30:37 programmer can program anything. 30:43 And by that, I mean anything that can be described in one 30:45 of these process, you can capture in that set of 30:47 primitives. 30:49 All right, the question is, as I started to say, is, "What 30:51 are the right primitives?" So there's a little bit of, a 30:54 little piece of history here, if you like. 30:55 In 1936, that same guy, Alan Turing, showed that with six 31:01 simple primitives, anything that could be described in a 31:05 mechanical process, it's actually algorithmically, 31:08 could be programmed just using those six primitives. 31:12 Think about that for a second. 31:14 That's an incredible statement. 31:16 It says, with six primitives, I can rule the world. 31:20 With six primitives, I can program anything. 31:23 A couple of really interesting consequences of that, by the 31:25 way, one of them is, it says, anything you can do in one 31:29 programming language, you can do in 31:31 another programming language. 31:33 And there is no programming language that is better-- well 31:36 actually, that's not quite true, there are some better at 31:37 doing certain kinds of things-- but there's nothing 31:39 that you can do in C that you can't do in Fortran. 31:43 It's called Turing compatibility. 31:45 Anything you can do with one, you can do with another, it's 31:46 based on that fundamental result. 31:49 OK. 31:50 Now, fortunately we're not going to start with Turing's 31:53 six primitives, this would be really painful programming, 31:56 because they're down at the level of, "take this value and 31:59 write it onto this tape." First of all, we don't have 32:01 tapes anymore in computers, and even if we did, you don't 32:04 want to be programming at that level. 32:05 What we're going to see with programming language is that 32:07 we're going to use higher-level abstracts. 32:09 A broader set of primitives, but nonetheless the same 32:12 fundamental thing holds. 32:13 With those six primitives, you can do it. 32:16 OK. 32:18 So where are we here? 32:19 What we're saying is, in order to do computation, we want to 32:22 describe recipes, we want to describe this sequence of 32:24 steps built on some primitives, and we want to 32:28 describe the flow of control that goes through those 32:30 sequence of steps as we carry on. 32:33 So the last thing we need before we can start talking 32:35 about real programming is, we need to 32:36 describe those recipes. 32:39 All right, And to describe the recipes, we're 32:41 going to want a language. 32:54 We need to know not only what are the primitives, but how do 32:57 we make things meaningful in that language. 33:01 Language. 33:03 There we go. 33:05 All right. 33:07 Now, it turns out there are-- 33:08 I don't know, John, hundreds? 33:09 Thousands? 33:10 Of programming languages? 33:11 At least hundreds-- of programming languages around. 33:13 PROFESSOR JOHN GUTTAG: [UNINTELLIGIBLE] 33:16 PROFESSOR ERIC GRIMSON: True. 33:16 Thank you. 33:18 You know, they all have, you know, 33:20 their pluses and minuses. 33:21 I have to admit, in my career here, I think I've taught in 33:23 at least three languages, I suspect you've taught more, 33:26 five or six, John? 33:27 Both of us have probably programmed in more than those 33:29 number of languages, at least programmed that many, since we 33:31 taught in those languages. 33:33 One of the things you want to realize is, 33:35 there is no best language. 33:36 At least I would argue that, I think John would agree. 33:38 We might both agree we have our own nominees for worst 33:40 language, there are some of those. 33:43 There is no best language. 33:44 All right? 33:44 They all are describing different things. 33:46 Having said that, some of them are better suited for some 33:48 things than others. 33:51 Anybody here heard of MATLAB Maybe programmed in MATLAB? 33:55 It's great for doing things with vectors and matrices and 33:58 things that are easily captured in that framework. 34:01 But there's some things that are a real 34:02 pain to do in MATLAB. 34:03 So MATLAB's great for that kind of thing. 34:05 C is a great language for programming things that 34:07 control data networks, for example. 34:10 I happen to be, and John teases me about this 34:12 regularly, I'm an old-time Lisp programmer, and that's 34:14 how I was trained. 34:16 And I happen to like Lisp and Scheme, it's a great language 34:19 when you're trying to deal with problems where you have 34:20 arbitrarily structured data sets. 34:23 It's particularly good at that. 34:25 So the point I want to make here is that there's no 34:27 particularly best language. 34:30 What we're going to do is simply use a language that 34:32 helps us understand. 34:33 So in this course, the language we're 34:34 going to use is Python. 34:38 Which is a pretty new language, it's growing in 34:39 popularity, it has a lot of the elements of some other 34:42 languages because it's more recent, it inherits things 34:44 from it's pregenitors, if you like. 34:48 But one of the things I want to stress is, this course is 34:50 not about Python. 34:54 Strange statement. 34:55 You do need to know how to use it, but it's not about the 34:58 details of, where do the semi-colons go in Python. 35:00 All right? 35:02 It's about using it to think. 35:04 And what you should take away from this course is having 35:06 learned how to design recipes, how to structure recipes, how 35:10 to do things in modes in Python. 35:13 Those same tools easily transfer 35:15 to any other language. 35:16 You can pick up another language in a week, couple of 35:18 weeks at most, once you know how to do Python. 35:22 OK. 35:23 In order to talk about Python and languages, I want to do 35:25 one last thing to set the stage for what we're going to 35:28 do here, and that's to talk about the different dimensions 35:30 of a language. 35:31 And there're three I want to deal with. 35:33 The first one is, whether this is a high-level 35:35 or low-level language. 35:41 That basically says, how close are you 35:42 the guts of the machine? 35:43 A low-level language, we used to call this assembly 35:45 programming, you're down at the level of, your primitives 35:48 are literally moving pieces of data from one location of 35:51 memory to another, through a very simple operation. 35:54 A high-level language, the designer has created a much 35:57 richer set of primitive things. 35:59 In a high-level language, square root might simply be a 36:02 primitive that you can use, rather than you having to go 36:04 over and code it. 36:06 And there're trade-offs between both. 36:08 Second dimension is, whether this is a general versus a 36:12 targeted language. 36:15 And by that I mean, do the set of primitives support a broad 36:18 range of applications, or is it really aimed at a very 36:22 specific set of applications? 36:23 I'd argue that MATLAB is basically a targeted language, 36:25 it's targeted at matrices and vectors and things like that. 36:29 And the third one I want to point out is, whether this is 36:31 an interpreted versus a compiled language. 36:41 What that basically says is the following: in an 36:44 interpreted language, you take what's called the source code, 36:46 the thing you write, it may go through a simple checker but 36:49 it basically goes to the interpreter, that thing inside 36:52 the machine that's going to control the flow of going 36:54 through each one of the instructions, 36:55 and give you an output. 36:57 So the interpreter is simply operating directly on your 37:00 code at run time. 37:02 In a compiled language, you have an intermediate step, in 37:05 which you take the source code, it runs through what's 37:07 called a checker or a compiler or both, and it creates what's 37:10 called object code. 37:11 And that does two things: one, it helps catch bugs in your 37:16 code, and secondly it often converts it into a more 37:19 efficient sequence of instructions before you 37:21 actually go off and run it. 37:24 All right? 37:24 And there's trade-offs between both. 37:25 I mean, an interpreted language is often easier to 37:27 debug, because you can still see your raw code there, but 37:30 it's not always as fast. A compiled language is usually 37:32 much faster in terms of its execution. 37:34 And it's one of the things you may want to trade off. 37:37 Right. 37:38 In the case of Python, it's a high-level language. 37:43 I would argue, I think John would agree with me, it's 37:45 basically a general-purpose language. 37:47 It happens to be better suited for manipulating strings than 37:50 numbers, for example, but it's really a 37:51 general-purpose language. 37:53 And it's primarily-- 37:55 I shouldn't say primarily, it is an interpreted language. 37:58 OK? 37:59 As a consequence, it's not as good as helping debug, but it 38:03 does let you-- sorry, that's the wrong way of saying-- it's 38:04 not as good at catching some things before you run them, it 38:07 is easier at some times in debugging as you go 38:09 along on the fly. 38:11 OK. 38:11 So what does Python look like? 38:13 In order to talk about Python-- actually, I'm going 38:15 to do it this way-- we need to talk about how to 38:22 write things in Python. 38:22 Again, you have to let me back up slightly and set the stage. 38:26 Our goal is to build recipes. 38:28 You're all going to be great chefs by the 38:29 time you're done here. 38:30 All right? 38:32 Our goal is to take problems and break them down into these 38:35 computational steps, these sequence of instructions 38:37 that'll allow us to capture that process. 38:40 To do that, we need to describe: not only, what are 38:42 the primitives, but how do we capture things legally in that 38:45 language, and interact with the computer? 38:47 And so for that, we need a language. 38:49 We're about to start talking about the elements of the 38:51 language, but to do that, we also need to separate out one 38:54 last piece of distinction. 38:58 Just like with a natural language, we're going to 38:59 separate out syntax versus semantics. 39:02 So what's syntax? 39:03 Syntax basically says, what are the legal expressions in 39:09 this language? 39:16 Boy, my handwriting is atrocious, isn't it? 39:22 There's a English sequence of words. 39:25 It's not since syntactically correct, right? 39:27 It's not a sentence. 39:28 There's no verb in there anywhere, it's just 39:30 a sequence of nouns. 39:31 Same thing in our languages. 39:32 We have to describe how do you put together legally formed 39:36 expressions. 39:38 OK? 39:39 And as we add constructs to the language, we're going to 39:41 talk about. 39:42 Second thing we want to talk about very briefly as we go 39:45 along is the semantics of the language. 39:48 And here we're going to break out two pieces; static 39:50 semantics and full semantics. 39:53 Static semantics basically says which programs are 40:01 meaningful. 40:05 Which expressions make sense. 40:09 Here's an English sentence. 40:17 It's syntactically correct. 40:20 Right? 40:20 Noun phrase, verb, noun phrase. 40:23 I'm not certain it's meaningful, unless you are in 40:25 the habit of giving your furniture personal names. 40:29 What's the point? 40:30 Again, you can have things that are syntactically legal 40:32 but not semantically meaningful, and static 40:35 semantics is going to be a way of helping us decide what 40:38 expressions, what pieces of code, actually have real 40:41 meaning to it. 40:41 All right? 40:43 The last piece of it is, in addition to having static 40:47 semantics, we have sort of full semantics. 40:53 Which is, what does the program mean? 40:58 Or, said a different way, what's going to 40:59 happen when I run it? 41:08 That's the meaning of the expression. 41:09 That's what you want. 41:10 All right? 41:10 You want to know, what's the meaning of this piece of code? 41:13 When I run it, what's going to happen? 41:14 That's what I want to build. 41:16 The reason for pulling this out is, what you're going to 41:18 see is, that in most languages, and certainly in 41:21 Python-- we got lots of help here-- all right, Python comes 41:29 built-in with something that will check your static, sorry, 41:31 your syntax for you. 41:33 And in fact, as a sidebar, if you turn in a problem set that 41:37 is not syntactically correct, there's a simple button that 41:40 you push that will check your syntax. 41:42 If you've turned in a program that's not syntactically 41:44 correct, the TAs give you a zero. 41:46 Because it said you didn't even take the time to make 41:48 sure the syntax is correct. 41:49 The system will help you find it. 41:50 In Python, it'll find it, I think one bug at 41:53 a time, right John? 41:53 It finds one syntax error at a time, so you have to be a 41:55 little patient to do it, but you can check that 41:57 the syntax is right. 41:59 You're going to see that we get some help here on the 42:06 static semantics, and I'm going to do an example in a 42:08 second, meaning that the system, some languages are 42:11 better than others on it, but it will try and help you catch 42:15 some things that are not semantically correct 42:20 statically. 42:21 In the case of Python, it does that I think all at run time. 42:23 I'm looking to you again, John, I think there's no 42:26 pre-time checks. 42:27 Its-- sorry? 42:27 PROFESSOR JOHN GUTTAG: [UNINTELLIGIBLE] 42:28 PROFESSOR ERIC GRIMSON: There is some. 42:31 OK. 42:32 Most of them, I think though, are primarily caught at run 42:35 time, and that's a little bit of a pain because you don't 42:36 see it until you go and run the code, and there are some, 42:38 actually we're going to see an example I think in a second 42:40 where you find it, but you do get some help there. 42:43 The problem is, things that you catch here are actually 42:47 the least worrisome bugs. 42:49 They're easy to spot, you can't run the program with 42:52 them there, so you're not going to get weird answers. 42:55 Not everything is going to get caught in 42:58 static semantics checking. 42:59 Some things are going to slide through, and 43:00 that's actually a bother. 43:03 It's a problem. 43:04 Because it says, your program will still give you a value, 43:07 but it may not be what you intended, and you can't always 43:10 tell, and that may propagate it's way down through a whole 43:12 bunch of other computations before it causes some 43:14 catastrophic failure. 43:16 So actually, the problem with static semantics is you'd like 43:19 it to catch everything, you don't always get it. 43:21 Sadly we don't get much help here. 43:23 Which is where we'd like it. 43:25 But that's part of your job. 43:27 OK. 43:27 What happens if you actually have something that's both 43:30 syntactically correct, and appears to have correct static 43:32 semantics, and you run it? 43:33 It could run and give you the right answer, it could crash, 43:37 it could loop forever, it could run and apparently give 43:43 you the right answer. 43:45 And you're not always going to be able to tell. 43:47 Well, you'll know when it crashes, that doesn't help you 43:49 very much, but you can't always tell whether 43:51 something's stuck in an infinite loop or whether it's 43:52 simply taking a long time to compute. 43:54 You'd love to have a system that spots that for you, but 43:57 it's not possible. 43:58 And so to deal with this last one, you 44:00 need to develop style. 44:02 All right? 44:06 Meaning, we're going to try to help you with how to develop 44:09 good programming style, but you need to write in a way in 44:12 which it is going to be easy for you to spot the places 44:15 that cause those semantic bugs to occur. 44:19 All right. 44:20 If that sounds like a really long preamble, it is. 44:23 Let's start with Python. 44:24 But again, my goal here is to let you see what computation's 44:28 about, why we need to do it, I'm going to remind you one 44:30 last time, our goal is to be able to have a set of 44:32 primitives that we combine into complex expressions, 44:35 which we can then abstract to treat as primitives, and we 44:38 want to use that sequence of instructions in this flow of 44:43 control computing, in order to deduce new information. 44:47 That imperative knowledge that we talked about right there. 44:50 So I'm going to start today, we have about five or ten 44:52 minutes left, I think, in order-- sorry, five minutes 44:54 left-- in order to do this with some beginnings of 44:56 Python, and we're going to pick this up obviously, next 44:58 time, so; simple parts of Python. 45:02 In order to create any kinds of expressions, we're going to 45:04 need values. 45:06 Primitive data elements. 45:07 And in Python, we have two to start with; we have numbers, 45:13 and we have strings. 45:16 Numbers is what you'd expect. 45:18 There's a number. 45:21 There's another number. 45:21 All right? 45:24 Strings are captured in Python with an open quote and some 45:28 sequence of characters followed by a closed quote. 45:33 Associated with every data type in Python is a type, 45:37 which identifies the kind of thing it is. 45:40 Some of these are obvious. 45:41 Strings are just a type on their own. 45:44 But for numbers, for example, we can have 45:46 a variety of types. 45:47 So this is something that we would call an 45:49 integer, or an INT. 45:50 And this is something we would call a 45:54 floating point, or a float. 45:57 Or if you want to think of it as a real number. 45:59 And there's some others that we can see. 46:01 We're going to build up this taxonomy if you like, but the 46:04 reason it's relevant is, associated with each one of 46:07 those types is a set of operators that expect certain 46:11 types of input in order to do their job. 46:13 And given those types of input, will get back output. 46:16 All right. 46:17 In order to deal with this, let me show you an example, 46:19 and I hope that comes up, great. 46:21 What I have here is a Python shell, and I'm going to just 46:25 show you some simple examples of how we start building 46:27 expressions. 46:28 And this'll lead into what you're going to see next time 46:30 as well as what you're going to do tomorrow. 46:32 So. 46:33 Starting with the shell, I can type in expressions. 46:37 Actually, let me back up and do this in video. 46:38 I can type in a number, I get back a number, I can type in a 46:42 string, I get back the string. 46:46 Strings, by the way, can have spaces in them, they can have 46:49 other characters, it's simply a sequence of things, and 46:52 notice, by the way, that the string five-- sorry, the 46:59 string's digit five digit two is different 47:02 than the number 52. 47:03 The quotes are around them to make that distinction. 47:05 We're going to see why in a second. 47:07 What I'm doing, by the way, here is I'm simply typing in 47:09 expressions to that interpreter. 47:11 It's using its set of rules to deduce the value and print 47:13 them back out. 47:15 Things I might like to do in here is, I might like to do 47:17 combinations of things with these. 47:19 So we have associated with simple things, a set of 47:23 operations. 47:27 So for numbers, we have the things you'd expect, the 47:32 arithmetics. 47:33 And let me show you some examples of that. 47:35 And actually, I'm going to do one other distinction here. 47:38 What I typed in, things like-- well, let me start this way-- 47:42 there's an expression. 47:44 And in Python the expression is, operand, operator, 47:48 operand, when we're doing simple expressions like this, 47:51 and if I give it to the interpreter, it gives me back 47:53 exactly what you'd expect, which is that value. 47:56 OK? 47:57 The distinction I'm going to make is, that's an expression. 47:59 The interpreter is going to get a value for it. 48:01 When we start building up code, we're 48:03 going to use commands. 48:04 Or statements. 48:05 Which are actually things that take in a value and ask the 48:08 computer to do something with it. 48:09 So I can similarly do this, which is going to look strange 48:13 because it's going to give me the same value back out, but 48:16 it actually did a slightly different thing. 48:17 And notice, by the way, when I typed it how print showed up 48:19 in a different color? 48:21 That's the Python saying, that is a command, that is a 48:24 specific command to get the value of the expression and 48:26 print it back out. 48:27 When we start writing code, you're going to see that 48:29 difference, but for now, don't worry about it, I just want to 48:31 plant that idea. 48:33 OK. 48:33 Once we've got that, we can certainly, though, 48:35 do things like this. 48:39 Notice the quotes around it. 48:42 And it treats it as a string, it's simply getting me back 48:44 the value of that string, 52 times 7, rather than 48:47 the value of it. 48:49 Now, once we've got that, we can start doing things. 48:52 And I'm going to use print here-- if I could type, in 48:53 order to just to get into that, I can't type, here we 48:55 go-- in order to get into the habit. 48:57 I can print out a string. 49:00 I can print out-- 49:07 Ah!-- 49:09 Here's a first example of something that 49:10 caught one of my things. 49:11 This is a static semantic error. 49:15 So what went on here? 49:16 I gave it an expression that had an operand in there. 49:18 It expected arithmetic types. 49:21 But I gave two strings. 49:23 And so it's complaining at me, saying, you can't do this. 49:26 I don't know how to take two strings and 49:27 multiply them together. 49:30 Unfortunately-- now John you may disagree with me on this 49:32 one-- unfortunately in Python you can, however, 49:34 do things like this. 49:37 What do you figure that's going to do? 49:39 Look legal? 49:41 The string three times the number three? 49:45 Well it happens to give me three threes in a row. 49:49 I hate this. 49:50 I'm sorry, John, I hate this. 49:51 Because this is overloading that multiplication operator 49:55 with two different tasks. 49:56 It's saying, if you give me two numbers, I'll 49:57 do the right thing. 49:58 If you give me a number and a string, I'm going to 50:00 concatenate them together, it's really different 50:02 operations, but nonetheless, it's what it's going to do. 50:05 STUDENT: [UNINTELLIGIBLE] 50:11 PROFESSOR ERIC GRIMSON: There you go. 50:12 You know, there will be a rebuttal phase a little later 50:14 on, just like with the political debates, and he 50:16 likes it as a feature, I don't like it, you can tell he's not 50:18 a Lisp programmer and I am. 50:20 All right. 50:21 I want to do just a couple more quick examples. 50:22 Here's another one. 50:23 Ah-ha! 50:24 Give you an example of a syntax error. 50:28 Because 52A doesn't make sense. 50:31 And you might say, wait a minute, isn't that a string, 50:32 and the answer's no, I didn't say it's a string by putting 50:34 quotes around it. 50:35 And notice how the machine responds differently to it. 50:38 In this case it says, this is a syntax error, and it's 50:41 actually highlighting where it came from so I can go 50:43 back and fix it. 50:44 All right. 50:45 Let's do a couple of other simple examples. 50:48 All right? 50:49 I can do multiplication. 50:50 I've already seen that. 50:51 I can do addition. 50:52 Three plus five. 50:54 I can take something to a power, double star, just take 50:58 three to the fifth power. 50:59 I can do division, right? 51:03 Whoa. 51:04 Right? 51:05 Three divided by five is zero? 51:08 Maybe in Bush econom-- no, I'm not going to do any political 51:10 comments today, I will not say that, all right? 51:12 What happened? 51:14 Well, this is one of the places where 51:15 you have to be careful. 51:16 It's doing integer division. 51:19 So, three divided by five is zero, with a 51:22 remainder of three. 51:23 So this is the correct answer. 51:24 If I wanted to get full, real division, I should make one of 51:28 them a float. 51:30 And yes, you can look at that and say, well is that right? 51:32 Well, up to some level of accuracy, yeah, that's .6 is 51:35 what I'd like to get out. 51:36 All right. 51:38 I can do other things. 51:40 In a particular, I have similar operations on strings. 51:46 OK, I can certainly print out strings, but I can actually 51:48 add strings together, and just as you saw, I can multiply 51:51 strings, you can kind of guess what this is going to do. 51:53 It is going to merge them together into one thing. 51:58 I want-- 51:59 I know I'm running you slightly over, I want to do 52:00 one last example, it's, I also want to be able to do, have 52:03 variables to store things. 52:05 And to do that, in this it says, if I have a value, I 52:11 want to keep it around, to do that, I can 52:12 do things like this. 52:20 What does that statement do? 52:21 It says, create a name for a variable-- which I just did 52:24 there, in fact, let me type it in-- mystring, with an equal 52:28 sign, which is saying, assign or bind to that name the value 52:32 of the following expression. 52:35 As a consequence, I can now refer to 52:37 that just by its name. 52:39 If I get the value of mystring, there it is, or if I 52:43 say, take mystring and add to it the string, mylastname, and 52:49 print it back out. 52:51 So this is the first start of this. 52:53 What have we done? 52:54 We've got values, numbers and strings. 52:57 We have operations to associate with them. 52:59 I just threw a couple up here. 53:00 You're going to get a chance to explore them, and you'll 53:02 see not only are there the standard numerics for strings, 53:04 there are things like length or plus or other things you 53:06 can do with them. 53:08 And once I have values, I want to get a hold of them so I can 53:10 give them names. 53:11 And that's what I just did when I bound that. 53:12 I said, use the name mystring to be bound to or have the 53:16 value of Eric, so I can refer to it anywhere else that I 53:19 want to use it. 53:20 And I apologize for taking you over, we'll come back to this 53:23 next time, please go to the website to sign up for 53:25 recitation for tomorrow.
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