Course Introduction and Administrative Information

• Course: 6100L (new number), Introduction to Programming
• Instructor: Anna Bell (first name Anna, last name Bell), EECS department lecturer ~10 years
• Full semester version of 6100a
• Lecture format: teaching + interactive Q&A + coding breaks ("utri breaks")
• Emphasis on participation and practice - programming is a skill requiring repetition
• Download slides beforehand for note-taking and coding along

Learning Objectives and Course Structure

• Three main outcomes: knowledge of concepts, programming skills, problem solving skills
• Lectures/exams: test conceptual knowledge
• Finger exercises: develop programming skills
• Problem sets: develop problem solving skills (English problem → CS concepts → programming)
• Topics covered:
  • Computational thinking
  • Python programming language
  • Code organization and structure
  • Basic algorithms
  • Algorithmic complexity and efficiency

Knowledge Types: Declarative vs Imperative

• Declarative knowledge: statements of fact (e.g., "square root of X is Y such that Y*Y = X")
• Imperative knowledge: recipes/how-to instructions
• Programming = writing recipes for computers
• Example: finding square root of 16
  • Initial guess: 3
  • Step 1: if 3*3 close enough to 16, stop; otherwise continue
  • Step 2: new guess = average of G and X/G = (3 + 16/3)/2 = 4.17
  • Step 3: repeat until close enough
  • Final result: ~4.00 (within acceptable tolerance)

Algorithms and Computer Architecture

• Algorithm components:
  • Sequence of steps
  • Flow of control (if/then, repeat)
  • Stopping condition
• Computers: machines that execute algorithms
  • Good at: storing lots of data, fast operations
  • Limitations: not smart, only follow instructions exactly
• Brief history:
  • Pre-1940s: fixed program computers (calculators)
  • Post-1940s: stored program computers with interpreters
• Basic computer components:
  • Memory (stores values at locations)
  • Arithmetic Logic Unit (ALU) - performs operations
  • Control unit with program counter
• Example low-level operations: add values at memory locations 3456 and 3457, store at 3458

Programming Language Fundamentals

• Alan Turing: anything computable in one language is computable in any other
• Six basic primitives: move tape left/right, read/write/erase value, no operation
• Programming language structure parallels with English:
  • Primitives: numbers, character sequences, operators
  • Syntax: valid combinations (object operator object)
  • Static semantics: meaningful combinations
  • Semantics: single meaning (unlike English ambiguity)
• Error types:
  • Syntactic: spelling, indentation
  • Static semantic: type mismatches
  • Semantic: program runs but produces wrong result

Python Objects and Types

• Programming = creating and manipulating objects
• Objects have types that determine allowed operations
• Scalar (primitive) types:
  • int: integers (5, 0, -100, 1000000)
  • float: real numbers (3.27, 2.0, -3.14159)
  • bool: Boolean values (True, False)
  • NoneType: special value None
• Type checking: use type() command
• Type casting: convert between types
  • float(3) → 3.0
  • int(5.2) → 5 (truncates, doesn't round)
  • round(5.9) → 6 (rounds then casts to int)

Expressions and Operations

• Expression syntax: object operator object
• Python evaluates expressions to single values, stores values not expressions
• Mathematical operations:
  • +, -, * : result type depends on operands (int if both int, float if any float)
  • / : always returns float
  • // : floor division (integer portion)
  • % : modulo (remainder)
  • ** : exponentiation
• Operator precedence: exponentiation, then *//%, then +/-
• Can use parentheses to override precedence

Variables and Assignment

• Variables: names bound to values (different from mathematical variables)
• Assignment syntax: variable = expression
• Process: evaluate right side first, then bind result to left side variable
• Variable naming rules and best practices:
  • Use descriptive names
  • Avoid single letters
  • Include comments for code chunks
• Assignment vs equality: = is assignment, not mathematical equality

Memory and Variable Binding

• Variables can be rebound to new values
• Example: radius = radius + 1
  • Evaluates right side: current radius value + 1
  • Binds result to radius variable
  • Loses previous binding
• Line-by-line execution: no automatic recalculation
• Python Tutor: useful tool for step-by-step code visualization

Practical Examples

• Circle calculations with pi, radius, area, circumference
• Unit conversion (meters to feet)
• Variable swapping problem:
  • Incorrect: x=y, y=x (loses original x value)
  • Correct: temp=y, y=x, x=temp (uses temporary variable)

Key Programming Concepts

• Computers only do what you tell them
• Programs execute line by line sequentially
• Objects have types that determine allowed operations
• Expressions evaluate to single values
• Variables provide readable names for values
• Assignment is binding, not equality