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Research Institute of Data Science and Vision Computing Machine Learning and Deep Learning Course

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Research Institute of Data Science and Vision Computing

Introduction

Research Institute of Data Science and Vision Computing 机器学习与深度学习课程作业

Content

assignment1

  • 基础:Git、Python基础学习
  • 算法:k-NN
  • 作业:CIFAR-10 图像分类

assignment2

  • 算法:Linear Regression (线性回归)
  • 优化:梯度下降法
  • 作业:Boston House Price Predict

assignment3

  • 算法:Logistic Regression(对数几率回归),sigmoid 函数
  • 作业:MNIST 手写数字识别
  • Bonus:完成Softmax CIFAR-10图像分类,以及类比 Softmax与Logistic的关系。

assignment4

  • 算法:Decision Tree
  • 作业:Decision Tree

assignment5

  • SVM介绍 (参考书籍:周志华机器学习)
  • 算法:SVM Hinge Loss
  • 作业:CIFAR-10图像分类

assignment6

  • Neural Network介绍
  • forward pass 、 backpropagation介绍
  • 作业:two-layer Neural Network CIFAR-10图像分类

assignment7

  • Neural Network 模块化实现
  • batch normalization介绍
  • dropout介绍
  • 作业:改进 Neural Network 代码、Batch Normalization、Dropout 实现

assignment8

  • CNN 介绍
  • 概念:卷积, Pooling, Stride, Padding, Learning Rate, Momentum, Softmax, ReLU, BP, SGD, Cross-Entropy Loss。
  • 作业:CIFAR-10图像分类。

Teacher

教师 -
秦品乐
助教 -
武宽 沈文祥

Participating students

Reference & Acknowledgements

我们的课程作业内容主要参考到了以下相关课程,在此对以下相关内容的作者表示感谢。

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Research Institute of Data Science and Vision Computing Machine Learning and Deep Learning Course

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