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Python代写|机器学习代写 COMP 307/420 — Introduction to AI


1 Objectives

The goal of this assignment is to help you understand the basic concepts and algorithms of machine learning, write computer programs to implement these algorithms, use these algorithms to perform classification tasks, and analyse the results to draw some conclusions. In particular, the following topics should be reviewed:

• Machine learning concepts,

• Machine learning common tasks, paradigms and methods/algorithms,

• Nearest neighbour method for classification,

• Decision tree learning method for classification,

• Perceptron/linear threshold unit for classification, and

• k-means method for clustering and k-fold cross validation for experiments.

These topics are (to be) covered in lectures 04–07. The textbook and online materials can also be checked.

2 Question Description

Part 1: Nearest Neighbour Method [30 Marks for COMP307, and 37 Marks for


This part is to implement the k-Nearest Neighbour algorithm, and evaluate the method on the wine dataset described below. Additional questions on k-means and k-fold cross validation need to be answered and discussed.

Problem Description

The wine data set is taken from the UCI Machine Learning Repository (http://mlearn.ics.uci.edu/MLRepository.html).

The data set contains 178 instances in 3 classes, having 59, 71 and 48 instances, respectively. Each instance has 13 attributes: Alcohol, Malic acid, Ash, Alcalinity of ash, Magnesium, Total phenols, Flavanoids, Non-flavanoid phenols, Proanthocyanins, Color intensity, Hue, OD280%2FOD315 of diluted wines, and Proline.

The data set is split into two subsets, one for training and the other for testing.