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# Hard Problems

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CS 65 // 2021-05-06

## Final Project
- Implementation due **Friday, May 14th at 5pm**

## Project Demo
- **When**: Thursday, May 13th at 9:30exam CT
- **Where**: Zoom
- 
 Every group will prepare a demonstration of their project on a laptop
- 
 Live demonstrations are preferable, but a presentation with slides is also sufficient
- 
 Think of it as a **poster presentation** where peers can stop by, ask you questions, see a demo, and discuss challenges you've encountered, etc.

## Project Demo

- When you are demoing your project, be prepared to:
    + 
 Give a short **elevator pitch** of your project which includes the theme, goals, and how far you are now in your project
    + 
 Give a live demonstration or slides demonstrating what the visuals would look like / how the user would interact /  etc.
- 
 Only half of the groups will be demoing at a time
    + 
 When you are not demoing, your job is to visit every other project
    + 
 Ask questions, give constructive feedback

# Questions
## ...about anything?

# Measuring Efficiency

## Various Sorting Algorithms

## Analyzing Efficiency
- Two most common metrics for efficiency:
- 
 **Time Complexity**
    + How much "time" it takes for an algorithm to complete
    + 
Usually measured in "number of basic steps" the algorithm takes
- 
 **Space Complexity**
    + How much "memory" the algorithm uses while it is working

## Big Oh Notation
- Counting the number of steps *exactly* is cumbersome
- 
 Big Oh notation allows us to specify time complexity neatly by ignoring all aspects except the fastest part
- 
 **Constant Time**: $O(1)$
- 
 **Logarithmic Time**: $O(\log(n))$
- 
 **Linear Time**: $O(n)$
- 
 **Quadratic Time**: $O(n^2)$
- 
 **Exponential Time**: $O(2^n)$

# Fibonacci Sequence

## Fibonacci Sequence
- The **Fibonacci sequence** is a recursively defined sequence of numbers that shows up everywhere
    + Branching of trees, flower pedals, ...
- 
 It is defined by the recursion:
    + $f(0) = 1$
    + $f(1) = 1$
    + $f(n) = f(n-1) + f(n-2)$
- 
 Yields the sequence: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, ...

# Hard Problems

# Reflections <br> on CS 65

## CS 65: Learning Goals
- **Problem Solving**
    + How to *think carefully* about computational problems
    + How to work towards a *solution*
    + How to *assess* solutions to the problem
- 
 **Computer Science:** Algorithms and Data Structures
    + How we represent and process data to solve problems

## CS 65: Learning Goals
- **Program and Software Design**
    + How to build software, or build better software
- 
 **General Thinking Skills**
    + Skills/approaches that you can apply in other situations

# What's next?

## Computer Science Core
- CS 65: Introduction to Computer Science I
- 
 CS 66: Introduction to Computer Science II
- 
 CS 67: Object-Oriented Programming
- 
 CS 130: Computer Organization and Assembly Language Programming
- 
 MATH 054: Discrete Mathematics
- 
 CS 137: Algorithms
- 
 CS 191: The Capstone Course

## Computer Science Electives
- CS 135: Programming Languages
- 
 CS 139: Theory of Computation
- 
 CS 143: Artificial Intelligence
- 
 CS 147: Computer Graphics
- 
 CS 160: Operating Systems
- 
 CS 167: Machine Learning
- 
 ... and many more!

# Course Evaluations