1 / 12

CSE332: Data Abstractions Lecture 28: Course Wrap-up

In this wrap-up lecture, we cover various essential data structures and algorithms studied throughout the course, including alternative balanced trees, priority queues, disjoint-set structures, and max-flow/min-cut graph algorithms. Key concepts such as AVL deletions and Huffman coding are discussed, emphasizing the foundational knowledge acquired. Students are encouraged to explore new data structures, understand runtime analysis, and leverage proper data tools in programming. Real-world applications span data management, graph theory, and concurrency, highlighting the continued relevance of these concepts in computing.

naida
Télécharger la présentation

CSE332: Data Abstractions Lecture 28: Course Wrap-up

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CSE332: Data AbstractionsLecture 28: Course Wrap-up Tyler Robison Summer 2010

  2. Merging So, we’ve spent the quarter exploring many different data structures; time to merge back together:

  3. Some unexplored nodes • Alternative data structures for balanced trees, priority queues • Disjoint-set data structure (union-find) • AVL deletion • Max-flow / min-cut graph algorithms • Huffman coding (compression)

  4. What we’ve covered • Really just a beginning • Many other priority queues: skew heap, leftist heap, binomial queue, … • Many other dictionaries: red/black tree, splay tree, … • Many variations on hash tables • Many variations on B-Trees • Many other sorts, graph algorithms, etc. • Just scratched the surface of concurrency • Run-time/recurrence-relation analysis go much deeper

  5. What we’ve covered • But we’ve covered the foundation • Many priority queues, but binary heaps are among the most common • Many dictionary data structures; among which balanced trees and hash tables are most important • Graph theory is an enormous area, but the basics will get you a long ways • Parallelism/concurrency issues covered are all that’s needed for many situations

  6. And now • You should now be equipped to • Learn new data structures & ADTs • ‘Once you learn one programming language, others come much easier’ • Understand/analyze run-times • Understand uses & trade-offs • In general make more informed use of them in programming • Have had experience writing/debugging/testing data structures & parallel/concurrent software • More experience with proofs • Maybe not up to proving P!=NP, but still • Know a bunch of tools, and know how to pick the right tool for the job

  7. Applications • Hash tables: Everywhere • Seriously, everywhere • If you’re interviewing for a programming job/internship, hash table questions are likely candidates • Data Bases: B-Trees under the hood • Graphs show up in CS again and again • Just very useful for modeling stuff: • Computer networks • Power grids • Road systems • Social networks • Knowledge/concept maps

  8. Applications • Parallelism & Concurrency • Increasingly important • Many more cores is likely the future of computing hardware • Programming for many cores is going to be important • Speed, and thus parallelism, hugely important in many areas • Games (Xbox 360: 3 cores) • Servers • Scientific/mathematical simulations • Many others; anything concerned with speed • Concurrency problems pop up even in some simple Java applications • Ex: Handling GUI events • Big Oh analysis: ubiquitous in CS • Now some specific examples in AI; trees & graphs

  9. Trees & Traversals • Problem space as tree • Want to find optimal solution • BFS & iterative deepening search both work well • Better technique called A* search: • Instead of ‘closest’ or ‘furthest’, choose lowest cost=g( )+h( ) • g( ) is cost so far • h( ) is expected distance to goal

  10. Decision Trees • Basis for simple decision-making agents • Algorithms to create optimal decision tree: • Take set of labeled data (‘Sunny,NormalHumidity,StrongWind: Yes’) • Uses ‘information gain’ to decide what attribute to ask about next • Of course, real decision trees likely to be much larger • Ex: Face detection features

  11. Neural Networks • Usually DAG of ‘neurons’ • Edges represent how information propagates from input nodes (observations) to output nodes (decision) • Uses include OCR: • Conceptually have each pixel as a binary input • Each output represents a character: ‘Is this image a 9?’

  12. Thanks! • Extra office hours

More Related