eugene87222 / NCTU-Machine-Learning

2019 fall

Machine Learning (2019.9 ~ 2020.1)

HW1

• Regularized linear model regression

  python3 ./HW1[_class].py <testfile>

HW2

• Naive Bayes classifier

  python3 ./HW2_1.py

• Online learning

  python3 ./HW2_2.py <testfile>

HW3

• Sequential Estimator

  python3 ./HW3_1&2.py <mean> <variance>

• Baysian Linear regression

  python3 ./HW3_3.py

HW4

• Logistic regression

  python3 ./HW4_1.py [<inputfile>]

• EM algorithm

  python3 ./HW4_2.py

HW5

• Gaussian Process

  python3 ./HW5_1.py

• SVM on MNIST dataset

  python3 ./HW5_2.py {1|2|3}

• Report

HW6

• Kernel K-Means

  ./KernelKMeans.sh <k-cluster> <imagename> {kmeans++|mod|random}

• Spectral Clustering

  python3 ./SpectralClustring.py <k-cluster> <imagename> {normalized|ratio} {kmeans++|random}

• Report

HW7

• Kernel Eigenfaces/Fisherfaces

  python3 ./HW7_1.py {1|2|3}

• t-SNE

  python3 ./HW7_2.py {tsne|ssne} <perplexity> <interval>

• Report