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EECS 280 Machine Learning
创建日期：2024-08-23 15:09:11
标签： EECS 280
Machine Learning

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EECS 280 Project 5: Machine Learning


In this project, you will write a program that uses natural language processing and machine

learning techniques to automatically identify the subject of posts from the EECS 280 Piazza. You

will gain experience with recursion, binary trees, templates, comparators, and the map data

structure. Another goal is to prepare you for future courses (like EECS 281) or your own

independent programming projects, so we have given you a lot of freedom to design the structure

of your overall application.

The correctness portion of the final submission is worth approximately 70%, with the remaining

approximately 30% based on the thoroughness of your BST test cases and style grading. Your

test cases and style will both by graded by the autograder.

Winter 2018: We will use the same automated style grading on this project that we did for project

4. On this project, the automated style checks will be part of the grade. To run the tests on your

own, check out the style checking tutorial.

You may work alone or with a partner. Please see the syllabus for partnership rules.

Table of Contents

Project Roadmap

Project Introduction

Project Essentials

The BinarySearchTree ADT

Testing BinarySearchTree

The Map ADT

Testing Map

The Piazza Datasets

Classifying Piazza Posts with NLP and ML

The Bag of Words Model

Training the Classifier

4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml

https://eecs280staff.github.io/p5-ml/ 2/21

Predicting a Label for a New Post

Implementing Your Top?Level Classifier Application

Classifier Application Interface

Output

Results

Appendix A: Map Example

Appendix B: Splitting a Whitespace?Delimited String

Project Roadmap

1. Set up your IDE

Use the tutorial from project 1 to get your visual debugger set up. Use this wget link

https://eecs280staff.github.io/p5‐ml/starter‐files.tar.gz .

Before setting up your visual debugger, you’ll need to rename each .h.starter file to a .h

file.

$ mv BinarySearchTree.h.starter BinarySearchTree.h

$ mv Map.h.starter Map.h

You’ll also need to create these new files and add function stubs.

$ touch main.cpp

These are the executables you’ll use in this project:

BinarySearchTree_compile_check.exe

BinarySearchTree_public_test.exe

BinarySearchTree_tests.exe

Map_compile_check.exe

Map_public_test.exe

main.exe

If you’re working in a partnership, set up version control for a team.

2. Read the Project Introduction and Project Essentials

4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml

https://eecs280staff.github.io/p5-ml/ 3/21

See the first sections below for an introduction to the project as well as essential instructions

for successfully completing the project.

3. Test and implement the BinarySearchTree data structure

We’ve provided header files with comments. Test and implement those functions. Be sure to

use recursion and tail recursion where the comments require it.

4. Test and implement the Map data structure

Implement and test a Map ADT that internally uses your BinarySearchTree to provide an

interface that works (almost) exactly like std::map from the STL! Appendix A has an example.

5. Test and implement the Piazza Classifier Application

This specification describes the interface for the overall application, but it’s up to you how to

separate it into functions and data structures.

Appendix B has tips and tricks for this part.

Submit to the Autograder

Submit the following files to the autograder.

BinarySearchTree.h

Map.h

main.cpp

BinarySearchTree_tests.cpp

Project Introduction

The goal for this project is to write an intelligent program that can classify Piazza posts according

to topic. This task is easy for humans ? we simply read and understand the content of the post,

and the topic is intuitively clear. But how do we compose an algorithm to do the same? We can’t

just tell the computer to “look at it” and understand. This is typical of problems in artificial

intelligence and natural language processing.

4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml

https://eecs280staff.github.io/p5-ml/ 4/21

We know this is about Euchre, but how can we write an algorithm that “knows” that?

With a bit of introspection, we might realize each individual word is a bit of evidence for the topic

about which the post was written. Seeing a word like “card”, “spades”, or even “bob” leads us

toward the Euchre project. We judge a potential label for a post based on how likely it is given all

the evidence. Along these lines, information about how common each word is for each topic

essentially constitutes our classification algorithm.

But we don’t have that information (i.e. that algorithm). You could try to sit down and write out a

list of common words for each project, but there’s no way you’ll get them all. For example, the

word “lecture” appears much more frequently in posts about exam preparation. This makes

sense, but we probably wouldn’t come up with it on our own. And what if the projects change? We

don’t want to have to put in all that work again.

Instead, let’s write a program to comb through Piazza posts from previous terms (which are

already tagged according to topic) and learn which words go with which topics. Essentially, the

result of our program is an algorithm! This approach is called (supervised) machine learning. Once

we’ve trained the classifier on some set of Piazza posts, we can apply it to new ones written in the

future.

4/5/2018 EECS 280 Project 5: Machine Learning | p5-ml

https://eecs280staff.github.io/p5-ml/ 5/21

Authors

This project was developed for EECS 280, Fall 2016 at the University of Michigan. Andrew DeOrio

and James Juett wrote the original project and specification. Amir Kamil contributed to code

structure, style, and implementation details.

Project Essentials

The project consists of three main phases:

1. Implement and test the static _impl member functions in BinarySearchTree .

2. Implement and test Map by using the has?a pattern on top of BinarySearchTree .

3. Design, implement, and test the top?level classifier application.

The focus of part 1 is on working with recursive data structures and algorithms. The framework

and some of the implementation for BinarySearchTree is provided for you, but you must

implement the core functionality in several static member functions. Be mindful of requirements

for which implementations must use certain kinds of recursion.

Part 2 should not require a lot of additional implementation code. Make sure to reuse the

functionality already present in BinarySearchTree wherever possible.

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