Yes, statistical machine learning appears to have several parents, grandparents, great grandparents, and great great grandparents--including the recursive modifications done to/with the family of regression tools.

https://www.kdnuggets.com/2017/06/regression-analysis-really-machine-learning.html

Scikit-Learn ： https://scikit-learn.org/stable/

Choosing the right estimator : https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html

11:00 AM ~ noon:

(1) Scikit-Learn 5 sklearn 的 datasets 数据库 (9:33)

https://www.youtube.com/watch?v=lXznUoPCJLM&list=PLXO45tsB95cI7ZleLM5i3XXhhe9YmVrRO&index=6

(2) Scikit-Learn 4 通用学习模式 (9:49)

https://www.youtube.com/watch?v=EvV99YhSsJU&list=PLXO45tsB95cI7ZleLM5i3XXhhe9YmVrRO&index=5

(3) Scikit-Learn 11 Save (机器学习 sklearn 教学教程tutorial) (11:26)

https://www.youtube.com/watch?v=8sMZkhWtdaI&list=PLXO45tsB95cI7ZleLM5i3XXhhe9YmVrRO&index=13

(4) 機器學習首部曲---Python實作_決策樹 (14:47)

https://www.youtube.com/watch?v=tfGuRVTwxq8&list=PLy7MS-q4l3xDYoR8MACYA3YyidUbEiz6j&index=3

(5) 機器學習首部曲---Python實作_隨機森林 (11:40)

https://www.youtube.com/watch?v=G8-MrVTYVgw&list=PLy7MS-q4l3xDYoR8MACYA3YyidUbEiz6j&index=5

GitHub: 2018年6月4日，微軟宣布以75億美元收購GitHub。

https://zh.wikipedia.org/wiki/GitHub

pyinvest/ml_toturial

https://github.com/pyinvest/ml_toturial

Homework: Boosting.ipynb

https://github.com/pyinvest/ml_toturial/blob/master/Boosting.ipynb

Homework: Principal-Component Analysis (PCA).ipynb

https://github.com/pyinvest/ml_toturial/blob/master/Principal-Component%20Analysis%20(PCA).ipynb

7月27日 (週二) : Multi-Layer Perception (MLP) - Iris, Kaggle

10:00 AM ~ 11:00 AM：

在這裡下載程式：人工智慧概論.zip

https://drive.google.com/drive/folders/1o7u9yEnRE4FmPDbUwwGV2bu0lX_lDHeI?usp=sharing

(1) 人工智慧概論 - 02, 原理 (49:09)

https://www.youtube.com/watch?v=iGhsZk9wE0I&list=PLYgGtiVoYLPcLDi-vW02DnSqmD6e6m6lL&index=3

(2) 人工智慧概論 - 12. 三種鳶尾花, 資料處理 (29:13)

https://www.youtube.com/watch?v=DoTwibz_R3I&list=PLYgGtiVoYLPcLDi-vW02DnSqmD6e6m6lL&index=13

11:10 AM ~ noon：

(3) 人工智慧概論 - 13. 三種鳶尾花, ANN - MLP (24:19)

https://www.youtube.com/watch?v=SHgFzlQgNhg&list=PLYgGtiVoYLPcLDi-vW02DnSqmD6e6m6lL&index=14

(4) 人工智慧概論 - 16, 三種鳶尾花, Save and Load, Model and Weights (15:02)

https://www.youtube.com/watch?v=hNdVJhgzAZw&list=PLYgGtiVoYLPcLDi-vW02DnSqmD6e6m6lL&index=17

(5) Kaggle ( https://www.kaggle.com/ )

* Kaggle Courses:

https://www.kaggle.com/learn

* GS-3073-AI-course, Regression or classification?

https://www.kaggle.com/c/gs-3073-ai-course/overview

* 透過去識別化之資料，學習課堂介紹之機械學習方法

https://www.kaggle.com/c/competition-on-introduction-to-ai

Homework: But what is a neural network? 究竟神經網路是什麼？ (19:13)

https://www.youtube.com/watch?v=aircAruvnKk

8月3日 (週二) : Convolutional Neural Network (CNN) - MNIST & CIFAR-10, CoLab

請在上課前觀看這段影片: But what is a neural network? 究竟神經網路是什麼？ (19:13)

https://www.youtube.com/watch?v=aircAruvnKk

10:00 AM ~ 10:50 AM:

台灣大學李宏毅老師的 Machine Learning 影片

==> ML Lecture 10: Convolutional Neural Network (1:19:28)

https://www.youtube.com/watch?v=FrKWiRv254g&list=PLJV_el3uVTsPy9oCRY30oBPNLCo89yu49&index=19

==> ML Lecture 7: Backpropagation (31:26)

https://www.youtube.com/watch?v=ibJpTrp5mcE&list=PLJV_el3uVTsPy9oCRY30oBPNLCo89yu49&index=12

課程開始 :

(1) MLP: MNIST - Deep Neural Network with Keras

https://www.kaggle.com/prashant111/mnist-deep-neural-network-with-keras

(2) MNIST digits classification dataset

https://keras.io/api/datasets/mnist/

(3) Simple MNIST convnet

https://keras.io/examples/vision/mnist_convnet/

參考資料 :

人工智慧概論 - 21, 數字辨識, MNIST - MLP (16:46)

https://www.youtube.com/watch?v=oxIJTop9Bc8&list=PLYgGtiVoYLPcLDi-vW02DnSqmD6e6m6lL&index=22

人工智慧概論 - 22, 數字辨識, MNIST - CNN (13:53)

https://www.youtube.com/watch?v=zO47OpmvVto&list=PLYgGtiVoYLPcLDi-vW02DnSqmD6e6m6lL&index=23

11:00 AM ~ noon:

(4) CIFAR-10 image classification with CNN (CPU vs GPU)

https://www.kaggle.com/bhuvanchennoju/cifar-10-image-classification-with-cnn

(5) Run "CIFAR-10 image classification with CNN" on Co-Lab

( 這部份的 Python 程式實作, 會在8月10日的影片中說明 )

參考資料 :

人工智慧概論 - 23, 數字辨識, MNIST - CNN, CoLab (14:00)

https://www.youtube.com/watch?v=3I9OBXOEe78&list=PLYgGtiVoYLPcLDi-vW02DnSqmD6e6m6lL&index=24

人工智慧概論 - 24, 影像分類, CIFAR10 - CNN, CoLab, Download files, ModelCheckpoint (29:24)

https://www.youtube.com/watch?v=mCstycWD3js&list=PLYgGtiVoYLPcLDi-vW02DnSqmD6e6m6lL&index=25

Homework: VGG16 Neural Network Visualization (7:35)

https://www.youtube.com/watch?v=RNnKtNrsrmg

8月10日 (週二) : Keras : ResNet, Autoencoder, 3D-CNN / Transfer Learning / YOLO4

== > 因為8月3日的課程進度落後, 所以在8月10日影片的前20分鐘, 是 Co-Lab 的程式實作.

10:00 AM ~ 11:00 AM:

(1) Run "CIFAR-10 image classification with CNN" on Co-Lab

At local computer:

import warningswarnings.simplefilter(action='ignore')from keras.datasets import cifar10import numpy as np# saving dateset(x_train, y_train),(x_test,y_test) = cifar10.load_data() np.savez('LOCAL-CIFAR10.npz', a=x_train, b=y_train, c=x_test, d=y_test)

On CoLab:

from google.colab import drivedrive.mount('/content/drive')
%cd /content!pwd%cd drive/My Drive/NCUAI-CIFAR10!ls -l
import numpy as np# loading datasetdata = np.load('LOCAL-CIFAR10.npz')x_train = data['a']y_train = data['b']x_test = data['c']y_test = data['d']