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# SES2020spring

## Introduction

This repository contains the complementary material for 项目实践 and 机器学习基础 in 2020 spring.
Due to the distributed and
asynchronous
nature of this study format, we will steer the course through a series of self-contained units around programming, visualization, and simulation, which may be your interest for your future endeavor or not. This course tries to fulfill this arrangement by introducing some tools and demonstrating their capacity.

4 units are planned for the first 4 weeks. Each unit will contain at least 2 parts. The 1st part will discuss general practice and methodology around software engineering and this serves as the general topic across the board. The second part will introduce some tools or library and show how to use them. It’s highly recommended that you should do your research about this tool or library before the classroom and practice with it after the class. The classroom time will be question/answer time and I will stay online to answer your question through Github issues, QQ, and Wechat.

## Unit 1: Introduction to Software Engineering and Git

In the introduction to software engneering, we will cover the concept of software, software engineering, and software crisis. We also show the basic workflow of Git, the distributed version control system.

## Unit 2: Agile Programming

We will have 2 parts in this unit. In the first part, we go through an agile method called Scrum. In this second part, we will talk about Python.

## Unit 3: Object Oriented Design and Programming

## Unit 4: Unit Test

## Final Project:

Please form a team with 2~3 members and pick a project from http://www.cse.msu.edu/~cse231/PracticeOfComputingUsingPython/.

## Q&A

Q: Do we have a syllabus for this course? What's the goal of this course?

A: For the situation caused by the Coronavirus, we do have an alternative plan for the course for the first 4 weeks. The topics are listed above. The goal of this course is to introduce several concept and practise around software development.

Q: The videos can be more connected and the title of the video is not clear?

A: We want each video to be self-contained and independent as possible so that referece to these video in the future makes more sense without refering to the context and the background. Acfun.cn and bilibili.com are meant to be used as video host only. Title and description for the video are only available on the site where this readme resides. This makes the material for this course neutral to video hosting site.

Q: There are some parts where complementary slides are not available?

A: The programming parts are mainly screen casting.

Q: Team up may be a better choice?

A: Exercise of some units will be for individual, while other exercises require a teamwork.

Q: What about students with a computer at his/her disposal?

A: We provide reference in each unit and some reading instruction. This is for students without computers.

## 机器学习基础

### 简介

1. Machine learning and related terms.
2. Types of problem discussed in machine learning.
3. Introduction to neural networks.

[video: introduction to machine learning @acfun](https://www.acfun.cn/v/ac15348189)

[video: introduction to machine learning @bilibili](https://www.bilibili.com/video/BV1Wz4y1R7dh/)

The material is in c1_intro.

### 逻辑回归

1. logistic regression
2. stochastic gradient descent
3. input normalization
4. oversampling/undersampling

[video for logistic regression, @acfun](https://www.acfun.cn/v/ac15394923)

[video for logistic regression, @bilibili](https://www.bilibili.com/video/BV1Ta4y1i7E4/)

[pdf for logistic regression](https://github.com/pipehappy1/SES2020spring/blob/master/C2_logistic_regression.pdf)

### Exercise 1

The task in this exercise is for you to apply logistic regression on ``Breast Cancer Wisconsin (Diagnostic) Data Set''.

* You can set train/test split to 0.7/0.3 or any value you like.
* You can choose your favorite tool besides PyTorch.
* Post your answer in a pull request to this repository.
* Discuss with each other while try to work the code out by yourself.

There is a piece of code demonstrating logistic regression using PyTorch. mlex1/lr.py

The data is available at https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

The data is also available on Kaggle: https://www.kaggle.com/uciml/breast-cancer-wisconsin-data

Note:

* UC Irvine Machine Learning Repository is the classic goto site to find some machine learning dataset.
* Kaggle is a popular machine learning competition site.

### 人工神经网络

1. Ensamble models by bagging/stacking/boosting
2. MLP by stacking logistic regression
3. Auto difference i.e. back propagation

Videos:

* [multiLayer-perceptron @acfun](https://www.acfun.cn/v/ac15453604)
* [multiLayer-perceptron @bilibili](https://www.bilibili.com/video/BV125411s7sH/)
* [back propagation @acfun](https://www.acfun.cn/v/ac15453964)
* [back propagation @bilibili](https://www.bilibili.com/video/BV1Ai4y147Aj/)

pdfs:

* [Perceptron and multiLayer-perceptron](https://github.com/pipehappy1/SES2020spring/blob/master/c3_mlp/c3_mlp.pdf)
* [Back propagation slides from Stanford cs231n](https://github.com/pipehappy1/SES2020spring/blob/master/c3_mlp/lecture_4.pdf)
* [Back propagation slides from Stanford cs231n (source)](http://cs231n.stanford.edu/slides/2020/lecture_4.pdf)

### 卷积网络

1. Convolutional operator in convolutional neural network (CNN)
2. Other layers often used in CNN
3. Learning tricks

Videos:

* [CNN @acfun](https://www.acfun.cn/v/ac15496062)
* [CNN @bilibili](https://www.bilibili.com/video/BV16K411W7ma)

pdf:

* [CNN](http://cs231n.stanford.edu/slides/2020/lecture_5.pdf)

### 其他神经网络模型

Videos:

* [pretrained CNN @acfun](https://www.acfun.cn/v/ac15537219)
* [pretrained CNN @bilibili](https://www.bilibili.com/video/BV1of4y117sk)

pdf:

* [pretrained CNN](http://cs231n.stanford.edu/slides/2020/lecture_9.pdf)

### Exercise 2

Please go through [deep learning with PyTorch: a 60 minute blitz](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html)
by yourself. You can post any question in the QQ chat group if you'd like to discuss.

* Use [conda](https://docs.conda.io/en/latest/miniconda.html) to manage and install the packages.
* Goto [PyTorch](https://pytorch.org/) to find the command to install PyTorch.
机器学习课程实验一

本实验中通过逻辑回归原理搭建了三层的神经网络，将570个样本中70%用于训练，30%用于测试

lr.py是集成了训练和测试的程序，data.csv是数据文件，使用使直接运行lr.py，会自行打印所需信息

最终测试结果为：

平均loss为0.32，正确率为：0.94
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