Lecture 1: Introduction to ECS 6100L

Instructor Information

• Instructor: Anna Belle (First name: Anna, Last name: Belle)
• Department: ECS
• Teaching Experience: 10 years

Course Overview

• Course Number: 6100L
• Semester: Full semester version of 6100A
• Focus: Introduction to programming with Python
• Materials: Download lecture slides beforehand, take notes, and run code during lectures
• Lecture Format: Mix of teaching, interactive coding (called "Utri Breaks"), and Q&A
• Importance of Participation: Emphasis on practice to develop programming skills

Key Goals

1. Knowledge of Concepts: Understanding fundamental computer science concepts
2. Programming Skill: Developing the ability to code in Python
3. Problem-Solving Skills: Translating problems from English into computational solutions

Topics Covered

1. Computational Thinking: Applying computation to solve problems
2. Python Programming: Basics to advanced usage
3. Code Organization: Structuring code for readability and modularity
4. Basic Algorithms: Introduction to foundational algorithms
5. Algorithmic Complexity: Assessing the efficiency and memory usage of programs

Types of Knowledge

• Declarative Knowledge: Statements of fact (less relevant for CS)
• Imperative Knowledge: Recipes for how to do something (crucial for programming)

Example: Finding the Square Root

1. Declarative Statement: The square root of X is Y such that Y * Y = X
2. Imperative Recipe: Approximate the square root using iterative guesses

What is an Algorithm?

• Sequence of Steps: Clear, defined steps to achieve a task
• Control Flow: Decisions and loops within the sequence (e.g., if-else, repeat-until)
• Termination: A clear stopping point for the algorithm

Computers and Programming Languages

• Fixed Program Computers: Hardwired for specific tasks (e.g., calculators)
• Stored Program Computers: Can store and execute instructions as data
• Turing Completeness: Any computable problem can be solved in any programming language
• Python Primitives: More convenient and comprehensive than basic computational primitives

Expressions and Types

• Expressions: Evaluated to produce a single value (e.g., 3 + 2 evaluates to 5)
• Types: Integers, floats, Boolean values, etc.
• Object Type Commands: Use type() to determine the type of an object
• Type Casting: Converting objects from one type to another (e.g., float(3) gives 3.0)

Variables and Assignment

• Variable Binding: Associating names with values for easier manipulation (e.g., pi = 3.14)
• Assignment Operator (=): Assigns the result of an expression to a variable
• Command Syntax: Object and command structure in Python (e.g., x = y + 1)
• Rebinding Variables: Changing the value associated with a variable (e.g., radius = radius + 1)

Errors and Debugging

• Syntax Errors: Errors in the structure of the code
• Static Semantic Errors: Invalid operations given the types involved
• Semantic Errors: Errors in the logic causing the program to produce unintended results

Function: Python Tutor

• Use: Step-by-step visualization of code execution to help with debugging

Summary

1. Objects and Types: Understanding and using Python's object types
2. Expressions: Creating and evaluating expressions
3. Variables: Using variables for storing and manipulating values
4. Assignments: Using assignment statements to bind variables to values
5. Control Flow Preview: Preparation for introducing decision points in future lectures

Next Steps

• Future Lectures: Introducing control flow constructs like if-else statements