CSC 301.01, Class 41: Wrapup
- Notes and news
- Upcoming work
- Extra credit
- More about the final
- Some learning goals/outcomes
News / Etc.
- Please attend Friday’s class.
- I’ve posted a sample final.
- Homework 10 due TONIGHT.
Extra Credit (Academic/Artistic)
Extra credit (Peer)
- Pub-free quiz, TONIGHT.
Extra Credit (Misc)
Other good things
- Musical this weekend.
- More music stuff.
- Should I submit really old extra credit writeups that are sitting on my computer?
- It couldn’t hurt.
- Will you be available next week?
- I should be sitting in my office much of the day on Monday and Tuesday.
More about the final
- Goal: A chance for you to demonstrate what you’ve learned/mastered. (See below.)
- Often, more “can you read and apply/critique an algorithm” than “can you design one”?
- We’ll look quickly at the sample final.
Some learning goals/outcomes
Note: In spite of what I said about EOCEs, we’re really doing this because I think it’s useful to step back and look at what you learned (or might have learned).
- Skills and strategies for designing algorithms and data structures.
- Increased knowledge of “the literature” of common algorithms and data structures.
- Other related stuff
Habits of thought in dealing with algorithm design
- Consider approaches
- Develop a potential algorithms to solve a problem
- Work though examples
- Try to find a counter-example
- Try to prove it correct
- Analyze it
- Do it all over again (“Can I do better?”)
Approaches to algorithm design
- Use one of the main techniques
- Divide and conquer
- Exhaustive search (and dynamic programming)
- Turn the problem into an already solved problem
- Plus …
- Drawing pictures can help
- Modify an existing algorithm
- Introduce randomness
- Restrict the input
- Accept approximations
“The literature” - Classic (and not so classic) algorithms and data structures
- Rabin-Karp - Substring matching based on hashing
- Shortest path - Solve the more general problem: Find the shotest path from s to every node. (Dijkstra’s)
- Prim - Minimum spanning tree (builds connected)
- Kruskal - Minimum spanning tree (builds disconnected)
- Ford-Fulkerson - Max flow
- Maximum bipartite
- Heap sort and other sorting algorithms
- Min stamps
- Approximate matching
- 0-1 knapsack
- Plus many many more.
- Skip list
- Trie (there are multiple implementations of tries, but we
usually use tries as a way to achieve an ADT or other goal)
- Used for those weird suffix tree things useful for pattern matching
- Balanced search trees
- Red-black trees
- 2:3 trees
- B tree (generalized 2:3 trees)
- Hash tables
- Different implementations of graphs
- Adjacency list
- Adjacency matrix
- Formalized asymptotic analysis - Big O, Big Omega, Big Theta
- A bit of amortized analysis
- Techniques for solving recurrence relations.
- Master theorem
- Recurrence trees
- Repeated expansion
- Way too many summations and rules for summations.
- Program verification
- Memory/state models
- Loop invariants
- Proof, informal and formal
- Induction (strong and weak)
- Counter-examples (covered earlier, but …)
- Practice with (stupid) hackerrank approaches
- Practice with C and GNU code conventions