Lecture 1 Notes: ECS 6100

Instructor Introduction

• Name: Anna Belle (Pronounced "Hannah Belle")
• Teaching for almost 10 years in the ECS department
• Excited to teach the full semester version of ECS 6100a

Lecture Overview

• Course administrative information
• Introduction to how computers work at a high level
• Basics of Python programming
• Encouraged to download lecture slides and take notes

Class Participation and Structure

• Utri Breaks: Interactive breaks for coding practice
• Encouragement of questions during lectures
• Coding practice is essential for learning
• Key outcomes:
  • Knowledge of computer science concepts
  • Programming skills
  • Problem-solving skills

Course Topics

• Computational Thinking: Approach problems mathematically or algorithmically
• Learning Python programming language
• Code structuring for readability and organization
• Basic algorithms and their complexities

Knowledge Types

• Declarative Knowledge: Statements of fact (not used in CS)
• Imperative Knowledge: Instructions/recipes for programming
  • E.g. Finding square roots with a specific algorithm
  • Algorithm Example: Steps to find the square root of a number

Computer Functions

• Computers execute algorithms
• They follow sequences of instructions
• Computers are good at:
  • Storing large data
  • Performing operations quickly
• Conclusion: Computers only do what you instruct them

Computing History Overview

• Pre-1940s: Fixed program computers (e.g., calculators)
• Post-1940s: Stored program computers that can execute instructions
  • Introduced interpreters for instruction execution

High-Level Computer Operations

• Computer has:
  • Memory (stores data)
  • Arithmetic Logic Unit (ALU) (does computations)
  • Control Unit (executes instructions in order)
• Example of a simple program illustrating memory and instruction flow

Programming Primitives

• Anything computable in one language is computable in another
• Python Primitives:
  • Include numbers, strings, operators, etc.

Syntax and Semantics in Programming

• Syntax: Correct structure (e.g., variable assignments)
• Static Semantics: Meaning of constructions
• Semantics: Intended meaning vs. actual execution result

Creating and Manipulating Objects in Python

• Objects have types (e.g., integers, strings) which dictate allowable operations
• Scalar Objects: Types include integers, floats, booleans, none
• Example of casting between types

Expressions and Variable Assignments

• Expressions evaluate to a single value, stored in memory
• Variable Assignment: Binding a name to a value (use =)
• Variables can be reassigned to new values

Naming Variables in Python

• Good naming makes code readable and maintainable
• Variable Naming Rules:
  • Must follow syntax rules, cannot start with a number, etc.

Summary of Key Concepts

• Programs create and manipulate objects in Python
• Understanding object types is crucial for correct operations
• Variables serve as named references to values, aiding code clarity
• Python executes instructions line by line; decisions are introduced later

Homework / Practice

• Explore Python Tutor for debugging

Next Steps

• Continue exploring decision points in programming next lecture