MIT OpenCourseWare: Introduction to Computer Science and Programming (Lecture 1)

Instructor Introduction

• Professor Eric Grimson and Professor John Guttag are lecturing.
• Both have extensive experience in Course 6 (Computer Science).
• Emphasis on taking the course seriously to learn about computation.

Lecture Goals

1. Administrivia: Course logistics and rules.
2. Course Objectives: Skills and knowledge students will gain by the end.
3. Introduction to Computational Thinking: Concepts to be covered.

Course Goals and Target Audience

Strategic Goals

1. Prepare Freshmen and Sophomores:

   • Ensure students can easily transition into Course 6.
   • No prior programming experience needed.
   • Not everyone needs to major in Course 6.

2. Confidence in Coding:

   • Equip all students to read and write small pieces of code.

3. Understanding of Computation:

   • Recognize its role in solving technical problems.

4. Competence and Confidence:

   • Ready students for internships and jobs, emphasizing computational problem-solving skills.

Tactical Goals

1. Basic Tools of Computational Thinking:

   • Writing small-scale programs.

2. Vocabulary of Computational Tools:

   • Ability to understand programs written by others.
   • Valuable for leveraging pre-existing code.

3. Understand Computational Limits:

   • Know what computations are possible and their costs.

4. Map Problems to Computational Frames:

   • Convert problems into computational terms for problem-solving.

Course Logistics

Class Structure

• Lectures: Tuesdays and Thursdays at 11:00 AM.
• Recitations: One hour weekly on Fridays (varied times).
• Outside Class Work: Approx. 9 hours per week (mainly problem sets in Python).

Quizzes and Exams

• Two Quizzes: Dates on the course website (open book/note).
• Final Exam: Scheduled during final exams period.
• Grading: 55% problem sets, 45% quizzes (10%, 15%, 20%).
• Problem Set 0: Initial setup, very easy to introduce Python.

Resources

• Problem Sets: Posted on the website.
• Class Notes: Not provided, encourages note-taking for better learning.
• Textbook: Optional, recommended book listed on the course site.
• Recitation Signup: Required through the course website.

Assistance

• TAs and Professors available for help and questions.
• Students encouraged to ask for help rather than struggle alone.

OpenCourseWare Videotaping

• Lectures are being recorded. Students not wanting to be recorded should sit in the back three rows.

Key Teaching Points

Computational Thinking

• Aim: Make computers do what you want them to do through computational problem-solving.
• Focus on computational modes of thought over memorization.

What is Computation?

• Separation of Mechanism and Thought:
  • Computation is not just the machine but the process and method.

Knowledge Classification

• Declarative Knowledge:
  • Statements of fact (e.g., mathematical definitions).
• Imperative Knowledge:
  • Process of deducing information (e.g., algorithms).

Early Computers

• Fixed-Program Computers:

  • Designed for specific tasks (e.g., calculators, early machines solving specific equations).

• Stored-Program Computers:

  • Flexibility to execute any sequence of instructions described in a program.

Modern Computing

• Interpreter:
  • Core of stored-program computers, enabling execution of sequences of instructions.

Architecture Overview

• Memory, Control Unit, ALU, Program Counter:
  • Descriptions stored as instructions in memory, executed sequentially, enabling complex computations.

Fundamental Primitives

• Alan Turing (1936): Showed with six basic primitives, any mechanical process can be described.
• Programming Languages: Different languages suited for different tasks.
• Python: Chosen for this course for its ease and general-purpose capabilities.
  • Emphasis on computational thinking rather than learning Python syntax.

Language Dimensions

• High-Level vs. Low-Level: Abstraction level relative to hardware.
• General vs. Targeted: Suitability for broad tasks vs. specific applications.
• Interpreted vs. Compiled: Execution method (line-by-line vs. pre-compiled).

Python as a Language

• High-Level, General-Purpose, Interpreted: Balances ease of learning and application flexibility.
• Focus on developing skills transferable to other languages.

Python Basics

Data Types

• Numbers: Integers, Floats.
  • int: Integer.
  • float: Real number with a decimal point.
• Strings: Sequence of characters enclosed in quotes.

Operations

• Arithmetic: Addition, subtraction, multiplication, division, exponentiation.
• String Operations: Concatenation, repetition.

Variables

• Assignment: Binding values to variable names using =.
• Stay tuned for more next lecture.

Summary

• Check course website for recitation sign-ups.
• Engage with course material and ask for help when needed.