NUS EE5907R - Pattern Recognition Assignment

• CA1

  Data Description

  The data is an email spam dataset, consisting of 4601 email messages with 57 features. Feature descriptions are found in this link. We have divided the data into a training set (3065 emails) and test set (1536 emails) with accompanying labels (1 = spam , 0 = not spam)

  Q1. Beta-bernoulli Naive Bayes

  Q2. Gaussian Naive Bayes

  Q3. Logistic regression

  Q4. K-Nearest Neighbors

  Q5. Survey

• CA2

  Data Description

  The project will be conducted on the MNIST dataset. The MNIST dataset contains in total 70,000 handwritten digit images, which are divided into a training set of 60; 000 examples, and a test set of 10,000 examples. The digits have been size-normalized and centered in a fixedsize image. The raw data and labels can be downloaded from in this link.

  Q1. PCA for Feature Extraction, Visualization and Classification

  Q2. LDA for Feature Extraction and Classification

  Q3. SVM for Classification

  Q4. Neural Networks

See report of CA1 and CA2 assignment.

CA1效果不是很好，CA2是满分。好好学习，学完就可以打游戏了~~~