changgyhub / machine-learning

Analysis and Implementation of Machine Learning Models and Methods

Machine Learning

Analysis and Implementation on Machine Learning Models and Methods for HKU Course COMP3314 by Dr. Li-Yi Wei.

Introduction

The Exercises include rigurous analysis of certain machine learning models, as well as implementation of them in python (sklearn and keras). View Here for a user-friendly revision of the course and my work. The original course pages are from 1iyiwei/pyml.

Contents

1 Warmup.ipynb: Sorting and Line fitting

2 Perceptron and SGD.ipynb: Linear perceptron and SGD

3 Kernel, Bayes and Models.ipynb: RBF kernel, Kernel SVM Complexity, Gaussian Bayes, and Basic Classifiers

4 Data Processing.ipynb: Sequential feature selection and PCA versus LDA

5 Training and Ensemble.ipynb: K-fold validation, Precision-recall curve, and Ensemble learning

6 Cluster and CNN.ipynb: Clustering for Digits and CNN on Cifar-10 (We use Wide ResNet and acheieved 94%)

Highlights