Mit course syllabus

Mit course syllabus DEFAULT

Syllabus

Course Meeting Times

Lectures: 2 sessions / week, 1 hour / session

Recitations: 1 sessions / week, 1 hour / session

Prerequisites

6.0001 Introduction to Computer Science and Programming in Python or permission of instructor.

Course Information

This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python programming language.

Note on 6.0001, 6.0002 and 6.00

  • 6.0001 (6 units): First half of term
  • 6.0002 (6 units): Second half of term
  • 6.00 (12 units): Full term

6.00 satisfies all degree / minor requirements that can be satisfied by taking both 6.0001 and 6.0002.

Students taking 6.00 will attend the 6.0001 and 6.0002 lectures and do the problem set for 6.0001 and 6.0002. The 6.0001 final will serve as a 6.00 midterm. The 6.0002 final will serve as the 6.00 final.

OCW has additional versions of 6.00 that include useful materials; this course will closely parallell the material covered in these versions:

Goals

  • Provide an understanding of the role computation can play in solving problems.
  • Help students, including those who do not necessarily plan to major in Computer Science and Electrical Engineering, feel confident of their ability to write small programs that allow them to accomplish useful goals.
  • Position students so that they can compete for research projects and excel in subjects with programming components.

Textbook

The textbook is Buy at MIT Press Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data. 2nd ed. MIT Press, 2016. ISBN: 9780262529624. [Preview with Google Books] The book and the course lectures parallel each other, though there is more detail in the book about some topics. It is available both in hard copy and as an e-book.

Lecture and Recitation Attendance

A significant portion of the material for this course will presented only in lecture, so students are expected to regularly attend lectures.

Recitations give students a chance to ask questions about the lecture material or the problem set for the given week. Sometimes, new material may be covered in recitation. Recitation attendance is encouraged but not required.

Problem Sets and Final Exam

Each problem set will involve programming in Python. There will be 5 problem sets in the course.

There will be one final exam. The exam is open book / notes but Not open Internet and Not open computer. Please print whatever you may want to use during the quiz.

Grading Policy

Grades will be roughly computed as follows:

ACTIVITIESPERCENTAGES
Problem sets50%
Completion of mandatory finger exercises10%
Final Exam40%

Problem sets will be graded out of 10 points. Submissions that do not run will receive at most 20% of the points.

Note: Finger exercises are not available on OCW.

Extension and Dropping Problem Sets Policy

We do not grant any extensions. Instead, we offer late days and the option of rolling at most 2 problem set grades into the final exam score.

Late Days

At the beginning of the term, students are given two late days that they can use on problem sets. Starting with Problem Set 1, additional late days can be accumulated for each assignment, one late day for each day the assignment is turned in ahead of the deadline. Up to three late days may be accumulated in this fashion in this course, i.e., you can only have a maximum of 3 late days at any point in time. Late days are discrete (a student cannot use half a late day). The staff will keep track of late days and feedback for each problem set will include the number of late days the student has remaining. Any additional late work beyond these late days will not be accepted. To avoid surprises, we suggest that after you submit your problem set, you double check to make sure the submission was uploaded correctly.

Rolling Over Problem Sets

As we assign final grades, we will maximize your score based on the choice to roll the weight of at most two problem sets into your final exam score. If rolled, the percent that the problem sets are worth will be rolled into the final exam score. We strongly urge you to see the late days and dropping the problem sets as backup in case of an emergency. Your best strategy is to do the problem sets early before work starts to pile up.

Calendar

SES #TOPICSKEY DATES
1Introduction and Optimization ProblemsProblem set 1 out
2Optimization Problems 
3Graph-theoretic Models 
4Stochastic Thinking

Problem set 1 due

Problem set 2 out

5Random Walks 
6Monte Carlo Simulation

Problem set 2 due

Problem set 3 out

7Confidence Intervals 
8Sampling and Standard Error

Problem set 3 due

Problem set 4 out

9Understanding Experimental Data

Problem set 4 due

Problem set 5 out

10Understanding Experimental Data (cont.) 
11Introduction to Machine Learning 
12Clustering 
13ClassificationProblem set 5 due
14Classification and Statistical Sins 
15Statistical Sins and Wrap UpFinal Exam

 

Sours: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/syllabus/

Syllabus

OCW Scholar

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About This Course

Let's start with the strategic goals of this course:

  • Help students (who may or may not intend to major in computer science) to feel justifiably confident of their ability to write small programs.
  • Map scientific problems into computational frameworks.
  • Position students so that they can compete for jobs by providing competence and confidence in computational problem solving.
  • Prepare college freshmen and sophomores who have no prior programming experience or knowledge of computer science for an easier entry into computer science or electrical engineering majors.
  • Prepare students from other majors to make profitable use of computational methods in their chosen field.

6.00SC can be summarized with these six major topics or objectives:

  • Learning a language for expressing computations—Python
  • Learning about the process of writing and debugging a program
  • Learning about the process of moving from a problem statement to a computational formulation of a method for solving the problem
  • Learning a basic set of "recipes"—algorithms
  • Learning how to use simulations to shed light on problems that don't easily succumb to closed form solutions
  • Learning about how to use computational tools to help model and understand data

6.00 is designed to help you become skillful at making the computer do what you want it to do. Once you acquire this skill, your first instinct when confronted with many tasks will be to write a program to do the task for you. Said another way, we want to help you learn to apply computational modes of thought to frame problems, and to guide the process of deducing information in a computational manner.

This means that the primary knowledge you will take away from this course is the art of computational problem solving. Unlike many introductory level courses, having an ability to memorize facts will be of little help in 6.00. This course is about learning to solve problems, not learning facts. (This, by the way, is exactly why all exams are open book.)

Prerequisites and Preparation

This course is aimed at students with little or no prior programming experience but a desire to understand computational approaches to problem solving. Now, by definition, none of you are under-qualified for this course. In terms of being over-qualified — if you have a lot of prior programming experience, we really don't want you wasting your time, and in this case we would suggest that you talk to me about how well this class suits your needs, and to discuss other options. In addition, we want to maintain a productive educational environment, and thus we don't want over-qualified students making other students feel inadequate, when in fact they are only inexperienced.

Since computer programming involves computational modes of thinking, it will help to have some mathematical and logical aptitude. You should be confident with your math skills up to pre-calculus.

Textbook

The original textbook for 6.00 and the course lectures parallel each other, though there is more detail in the book about some topics. The book is NOT required. We will not be referring to it in assignments or depending upon it to cover holes in the lectures.

Buy at MIT Press Guttag, John. Introduction to Computation and Programming Using Python. Spring 2013 edition. MIT Press, 2013. ISBN: 9780262519632.

A second edition of the textbook is now available. However, there may be some discrepancies between the original course lectures included on this course site and the sections in this second edition of the textbook.

Buy at MIT Press Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press, 2016. ISBN: 9780262529624.

If you choose not to purchase the textbook, you will probably find it useful to buy or borrow another book that covers Python. You might check your local public library's resources, or search online for a free Python text, such as How to Think Like a Computer Scientist or This resource may not render correctly in a screen reader.An Introduction to Python (PDF).

Online readings will be posted on the appropriate session pages. A more complete list of readings and references (not all of which are specifically assigned during lectures) can be found in the References section.

Technical Requirements

Since one of the goals of this course is to become familiar with programming, you will need to install and use the Python programming language and the interpreter IDLE. Please see the Software section for information and instructions on downloading the required software.

Most lectures involve programming demonstrations, and the code involved will generally be posted twice: once as a handout in PDF format, and again as a code file in .PY (Python) format. Additionally, many problem sets have accompanying code required for completing the assignment, and these are posted as .PY (Python) files. If you do not have the software installed, you will not be able to properly open and use these files.

Acknowledgments

We would like to thank course TAs Mitchell Peabody, Gartheeban Ganeshapillai, and Sarina Canelake for their participation in filming 6.00 recitations for OCW Scholar, and Niki Castle and Elaina Cherry for their work and dedication adapting the 6.00 materials for Scholar students. We would also like to thank Eric Grimson for his role in the development of 6.00 teaching material over the years, and for allowing us to record a guest lecture.

 

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Sours: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00sc-introduction-to-computer-science-and-programming-spring-2011/syllabus/
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Some Description

Instructor(s)

Prof.

As Taught In

Spring 2002

Course Number

2.24

Level

Undergraduate/Graduate

Features

Lecture Notes, Student Work

Sours: https://ocw.mit.edu/courses/mit-curriculum-guide/
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Syllabus

Course Meeting Times

Lectures: 2 sessions / week, 1 hour / session

Recitations: 1 sessions / week, 1 hour / session

Course Information

6.0001 Introduction to Computer Science and Programming in Python is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python 3.5 programming language.

This is a half-semester course. Students who successfully complete 6.0001 may continue into 6.0002 Introduction to Computational Thinking and Data Science, which is taught in the second half of the semester.

Goals

  • Provide an understanding of the role computation can play in solving problems.
  • Help students, including those who do not plan to major in Computer Science and Electrical Engineering, feel confident of their ability to write small programs that allow them to accomplish useful goals.
  • Position students so that they can compete for research projects and excel in subjects with programming components.

Textbook

The textbook is Buy at MIT Press Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data Second Edition. MIT Press, 2016. ISBN: 9780262529624. The book and the course lectures parallel each other, though there is more detail in the book about some topics. It is available both in hard copy and as an e-book.

Lecture and Recitation Attendance

A significant portion of the material for this course will presented only in lecture, so students are expected to regularly attend lectures.

Recitations give students a chance to ask questions about the lecture material or the problem set for the given week. Sometimes, new material may be covered in recitation. Recitation attendance is encouraged but not required.

Problem Sets and Quizzes

Each problem set will involve programming in Python. There will be 6 problem sets in the course. There will be two quizzes. All quizzes will be closed-book, though you will be allowed to bring one page of notes to the first quiz and two pages of notes to the second quiz. Pages must be letter-sized, double-sided, either handwritten or typed.

Grading Policy

Grades will be roughly computed as follows:

ACTIVITIESPERCENTAGES
Problem sets30%
Completion of mandatory finger exercises10%
Midterm Quiz20%
Final Quiz40%

Problem sets will be graded out of 10 points. Submissions that do not run will receive at most 20% of the points. Please contact your Teaching Assistant if you have a problem understanding your problem set grade.

Note: Quizzes and finger exercises are not available on OpenCourseWare.

Extension and Dropping Problem Sets Policy

We do not grant any extensions. Instead, we offer late days and the option of rolling at most 2 problem set grades into the final quiz score.

Late Days

At the beginning of the term, students are given two late days that they can use on problem sets. Starting with Problem Set 1, additional late days can be accumulated for each assignment, one late day for each day the assignment is turned in ahead of the deadline. Up to three late days may be accumulated in this fashion in this course, i.e you can only have a maximum of 3 late days at any point in time. Late days are discrete (a student cannot use half a late day). The staff will keep track of late days and feedback for each problem set will include the number of late days the student has remaining. Any additional late work beyond these late days will not be accepted. To avoid surprises, we suggest that after you submit your problem set, you double check to make sure the submission was uploaded correctly.

Rolling Over Problem Sets

Before the final quiz, we will send out an announcement in which you can choose at most 2 problem sets that you can drop. If dropped, the percent that the problem sets are worth will be rolled into the final quiz score. We strongly urge you to see the late days and dropping the problem sets as backup in case of an emergency. Your best strategy is to do the problem sets early before work starts to pile up.

Calendar

SES #TOPICSASSIGNMENTS
1What is computation?Pset 0 released
2Branching and IterationPset 1 released
3String Manipulation, Guess and Check, Approximations, BisectionPset 0 due
4Decomposition, Abstractions, FunctionsPset 2 released
5Tuples, Lists, Aliasing, Mutability, CloningPset 1 due
6Recursion, DictionariesPset 3 released
7Testing, Debugging, Exceptions, AssertionsPset 2 due; Quiz 1
8Object Oriented Programming 
9Python Classes and InheritancePset 3 due; Pset 4 released
10Understanding Program Efficiency, Part 1Pset 4 due; Pset 5 released
11Understanding Program Efficiency, Part 2 
12Searching and SortingPset 5 due; Final Quiz
Sours: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/syllabus/

Course syllabus mit

Syllabus

Course Meeting Times

Lectures: 2 sessions / week, 1 hour / session

Recitations: 2 sessions / week, 1 hour / session

Course Description

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

Prerequisites

A firm grasp of Python and a solid background in discrete mathematics are necessary prerequisites to this course. You are expected to have mastered the material presented in 6.01 Introduction to EECS I and 6.042J Mathematics for Computer Science.

If you have not taken and been successful in each of these subjects, please speak with a TA or professor before enrolling. We do allow students who have equivalent, other experience with the material described above to enroll, but with the firm understanding that mastery of this material is assumed and that course staff will not feel obligated to cover it or to help students who are struggling with it.

6.006 is a 12-unit (4-0-8) subject and serves as a Foundational Computer Science subject under the new curriculum. It is a direct prerequisite for 6.046 Design and Analysis of Algorithms, the theory header.

Textbooks

Required

Buy at MIT Press Cormen, Thomas, Charles Leiserson, Ronald Rivest, and Clifford Stein. Introduction to Algorithms. 3rd ed. MIT Press, 2009. ISBN: 9780262033848.

For the student who finds books helpful, we also suggest:

Miller, Bradley, and David Ranum. Problem Solving with Algorithms and Data Structures Using Python. 2nd ed. Franklin, Beedle & Associates, 2011. ISBN: 9781590282571.

Software

6.006 programming environment setup

Lectures and Recitations

One-hour lectures are held twice a week. You are responsible for material presented in lectures, including oral comments made by the lecturer (or other information that may not be present in the notes).

One-hour recitations are held twice a week, one day after the lectures. You are responsible for the material presented in recitation, which may include new material not presented in lectures. Recitation attendance has been well-correlated with quiz performance in past semesters. Recitations also give you a more intimate opportunity to ask questions of and to interact with the course staff. Your recitation instructor is responsible for determining your final grade.

Problem Sets

We will assign seven problem sets during the course of the semester. Each problem set will consist of a programming assignment, to be completed in Python, and a theory assignment.

If you collaborate with others in any fashion, you must list their names as collaborators. For details, please see the section on our collaboration policy; we take this very seriously.

Late assignments will be severely penalized. (This penalty is currently a 1% deduction every six minutes or part thereof until the end of the tenth hour after the deadline, after which submissions will receive no credit.)

Quizzes

We will give two evening quizzes during the semester; these will each be two hours in duration. There will also be a final exam during finals week.

Grading Policy

Your final grade will be determined by the grades you receive on problem sets, on quizzes, and on the final. The particulars of this policy are subject to the discretion of the course staff.

ACTIVITIESPERCENTAGES
Problem sets30%
Quizzes20% each
Final exam30%

Coding Assignments

The code that you hand in will be graded based on its correctness, its quality, and the details of the algorithm that it implements.

Correctness

We will provide a set of public unit tests with each problem to help you test your work. However, when grading, we will use additional unit tests that will not be available to you; we reserve the right to test any behavior specified by or following from the problem statement. Submissions that run for excessive amounts of time may be scored as incorrect.

Theory

Code should represent an implementation of an appropriately designed algorithm. While we do not necessarily expect you to achieve any lower bounds that may exist for a particular problem, submissions should not be overly inefficient in either time or space.

Copying another student's code is considered cheating. We may use both manual and automated methods to detect cheating.

Written Assignments

We expect you to enter proofs using LaTeX math mode directly into Gradetacular. We have a two-step process for grading proofs. First, you'll enter your proof into Gradetacular before the time that the problem set is due. We will provide the solutions 10 hours after the problem set is due, which you will use to find any errors in the proof that you submitted. Your critique will usually be due by the following lecture. Your grade will be based on your solution and your critique.

The same late policy applies to the grading part of the assignment (1% off every six minutes that the problem set is late). Please note that if you require an extension, we will need to know in advance and you must have a good reason for needing it. In addition, we trust that you will not look at the posted solutions when completing the problem set under an extension. Looking at the solutions under these conditions constitutes a breach of the honor code, and is a serious offense.

The best responses will be concise, correct, and complete. Failing to answer part of the question, being overly verbose, missing special or edge cases, and answering mistakenly will each reduce your score.

When you are called upon to "give an algorithm," you must provide (1) a textual description of the algorithm, and, if helpful, pseudocode; (2) at least one worked example or diagram to illustrate how your algorithm works; (3) a proof (or other indication) of the correctness of the algorithm; and (4) an analysis of the time complexity (and, if relevant, the space complexity) of the algorithm.

Remember that, above all else, your goal is to communicate. After all, if a grader cannot understand your solution, they cannot give you any credit for it.

Collaboration Policy

The goal of homework is to give you practice in mastering the course material. Consequently, you are encouraged to collaborate on problem sets. In fact, students who form study groups generally do better on exams than do students who work alone. If you do work in a study group, however, you owe it to yourself and your group to be prepared for your study group meeting. Specifically, you should spend at least 30–45 minutes trying to solve each problem beforehand.

You must write up each problem solution by yourself without assistance, even if you collaborate with others to solve the problem. You are asked on problem sets to identify your collaborators. If you did not work with anyone, you should write that you did not have collaborators. If you obtain a solution through research (e.g., on the web), acknowledge your source, but write up the solution in your own words. It is a violation of this policy to submit a problem solution that you cannot orally explain to a member of the course staff.

Code you submit must also be written by yourself. You may receive help from your classmates during debugging. Don't spend hours trying to debug a problem in your code before asking for help. However, regardless of who is helping you, only you are allowed to make changes to your code. Both manual and automatic mechanisms will be employed to detect plagiarism in code.

No other 6.006 student may use your solutions; this includes your writing, code, tests, documentation, etc. It is a violation of the 6.006 collaboration policy to permit anyone other than 6.006 staff and yourself to see your solutions to either theory or code questions.

Plagiarism and other anti-intellectual behavior cannot be tolerated in any academic environment that prides itself on individual accomplishment. If you have any questions about the collaboration policy, or if you feel that you may have violated the policy, please talk to one of the course staff. Although the course staff is obligated to deal with cheating appropriately, we often have the ability to be more understanding and lenient if we find out from the transgressor himself or herself rather than from a third party.

Sours: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/syllabus/
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