Week 3 (09/29) : Machine Learning for Classification

Lecture Note : Introduction to Machine Learning (GSlide, PDF)

Homework : Python (I)

Readings :

• Machine Learning - Wikipedia.

• Artificial Intelligence, Machine Learning, and Deep Learning — What the Difference? 2020.

• A casual intro to Machine Learning, 2017.

• Understanding the differences between AI, machine learning, and deep learning, 2017.

• What's the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? nVidia, 2016.

• How Machine Learning Works, As Explained by Google, 2015.

• Chapter 1 Introduction, Introduction to Machine Learning, 2nd, E. Alpaydin, MIT Press, 2010.

• A Friendly Introduction to Machine Learning, Udacity 2016. Youtube video 30'52".

Lecture Note : Machine Learning for Pattern Recognition (GSlide, PDF)

Homework : Python (II)

Readings :

• Machine Learning for Image Recognition, News 2017.

• Machine Learning in Computer Vision, Applied Artificial Intelligence, Vol. 15, pp. 693-705, 2001.

• Classification: Probabilistic Generative Model (pdf, video) Machine Learning課程，台大電機系李宏毅教授，2017.

Lecture Note : Machine Learning Problems and Algorithms (GSlide, PDF)

Homework : Python (III)

Readings :

• Part 1 Overview, Goals, Learning Types, and Algorithms , Machine Learning: An In-Depth, Non-Technical Guide. 2017.

• Top 10 Machine Learning Algorithms, 2016

• 關於 Machine Learning 與 Statistical Modeling 的那些事, 2016.

Lecture Note : Machine Learning Problems and Algorithms (GSlide, PDF)

Homework : None

Readings :

• Thinking about Data, Victor Lavrenko, 2015. Lectures 2 & 3 of the Introductory Applied Machine Learning (IAML) course at the University of Edinburgh (25 部Youtube影片約80分鐘)

• Chapter 1 Introduction, Introduction to Machine Learning, 2nd, E. Alpaydin, MIT Press, 2010.

• Chapter 1 Introduction, Machine Learning: An Algorithmic Perspective, S. Marsland, Chapman and Hall/CRC, 2009.

Lecture Note : Supervised Learning (GSlide, PDF)

Homework : P1 TensorFlow Basics

Readings :

• Supervised Learning - Wikipedia

• Comparing supervised learning algorithms, 2015.

• Predictive modeling, supervised machine learning, and pattern classification - the big picture, 2014.

• Supervised learning superstitions cheat sheet

Lecture Note : Supervised Learning (GSlide, PDF)

Homework : P2 TensorFlow Basics II

Readings :

• Chapter 2 Supervised Learning, 2nd, E. Alpaydin, MIT Press, 2010.

• Chapters 1-7, Machine Learning: An Algorithmic Perspective, S. Marsland, Chapman and Hall/CRC, 2009.

• Part 1 Classification: Chapters 1-7, Machine Learning in Action. Peter Harrington, Manning Publications Co., 2012.

Lecture Note : Linear Regression (GSlide, PDF)

Homework : P3 Doing Math with Tensorflow

Readings :

• Linear Regression - Wikipedia

• 線性迴歸的運作原理，資料科學・機器・人, 2017。 Source : How linear regression works, Data Science and Robots Blog, 2016. Youtube video.

• An Introduction to Gradient Descent and Linear Regression, 2014. (Python)

• Chapter 8 Predicting numeric values: regression, Machine Learning in Action. by Peter Harrington, Manning Publications Co., 2012.

• Lecture Series: Linear Regression, 3 部Youtube影片 約50分鐘, 2016.

• Lesson 1: Simple Linear Regression, STAT 501, Eberly College of Science, The Pennsylvania State University.

• Machine Learning，李宏毅，台大電機，2016. Regression: Case Study, pdf, video

Lecture Note : Logistic Regression (GSlide, PDF)

Homework : P4 Starting with Machine Learning

Readings :

• Chapter 5 Logistic regression, Machine Learning in Action, by Peter Harrington, Manning Publications Co., 2012. (PDF)

• Logistic and Softmax Regression, Xianxu Hou's blog, 2015

• Deep Learning Tutorial: Linear Regression, Logistic Regression, Softmax regression.

• What is Softmax Regression and How is it Related to Logistic Regression? 2016.

Lecture Note : Logistic Regression (GSlide, PDF)

Homework : P5 Introducing Neural Networks

Readings :

• Chapter 5 Logistic regression, Machine Learning in Action, by Peter Harrington, Manning Publications Co., 2012. (PDF)

• Logistic and Softmax Regression, Xianxu Hou's blog, 2015

• Deep Learning Tutorial: Linear Regression, Logistic Regression, Softmax regression.

• What is Softmax Regression and How is it Related to Logistic Regression? 2016.

Lecture Note : Neural Information Processing (GSlide, PDF)

Homework : P6 Deep Learning

Readings :

• Machine Learning: An Algorithmic Perspective, S. Marsland, Chapman and Hall/CRC, 2009.

  • Chapter 2 Linear Discriminants (PDF)

  • Chapter 3 The Multi-Layer Perceptron (PDF)

• Chapter 11 Multilayer Perceptrons, Introduction to Machine Learning, 2nd, E. Alpaydin, MIT Press, 2010. (PDF)

Lecture Note : Backpropagation (GSlide, PDF)

Homework : P6 Deep Learning

Readings :

• Machine Learning - Neural Networks Tutorial, Paul Tero of Existor Ltd, 2015/8/20

• Backpropagation in 5 Minutes, Youtube:00:05:28. 2017/04/02. (中文字幕)

• Neural Networks, Youtube video channel (中文字幕)

  • 1. What is Neural Network (19’13")

  • 2. How Neural Networks Learn (21’01")

  • 3. What Is Backpropagation really doing? (13’54")

  • 4. Backpropagation Calculus (10’18")

• Backpropagation單元 pdf, video, Machine Learning課程，李宏毅，台大電機，2017.

• Chapter 3 The Multi-Layer Perceptron (PDF), Machine Learning: An Algorithmic Perspective, S. Marsland, Chapman and Hall/CRC, 2009.

• Neural Networks & The Backpropagation Algorithm, Explained, 2016/03/03.

• 資料科學・機器・人, 2017：神經網路的運作原理，反向傳播的運作原理。

  • How Deep Neural Networks Work, B. Rohrer, 2017/03/02. Youtube 24"37'. Blog. How neural networks work.

• The Evolution of Gradient Descent, Youtube:00:09:18, 2017/06/02. (此英文的簡短介紹不僅有gradient descent，還有其他深度神經網路常用的方法)

• Backpropagation - Wikipedia

Lecture Note : Deep Learning Introduction (GSlide, PDF)

Homework : Project Announcement

Readings :

• Neural network (backpropagation) playground by TensorFlow (1 hidden layer, activation=tanh, batch size=10)

• 資料科學・機器・人, 2017：解密深度學習，卷積神經網路的運作原理。

• Machine Learning and Deep Learning : Differences, 2018/09/28.

• Deep Learning模型最近若干年的重要进展，2018.

• Deep Learning for Computer Vision – Introduction to Convolution Neural Networks, 2016.

• Machine Learning，李宏毅，台大電機，2016.

  • Brief Introduction of Deep Learning pdf, video

  • Tips for deep learning pdf, video

  • Why deep pdf, video

Lecture Note : Convolutional Neural Network (CNN) (GSlide, PDF)

Homework : Project proposal

Readings :

• Tutorial: Derivation of Convolutional Neural Network from Fully Connected Network, 2018/07/31.

• 解析卷积神经网络—深度学习实践手册，2017 (51MB)

• How Convolutional Neural Networks Work, B. Rohrer, 2016/08/18. Youtube 26'13". Blog.

• Machine Learning，李宏毅，台大電機，2016. Convolutional Neural Network pdf, video

Lecture Note : Convolutional Neural Network (CNN) (GSlide, PDF)

Homework : None

Readings :

• Tutorial: Derivation of Convolutional Neural Network from Fully Connected Network, 2018/07/31.

• 解析卷积神经网络—深度学习实践手册，2017 (51MB)

• How Convolutional Neural Networks Work, B. Rohrer, 2016/08/18. Youtube 26'13". Blog.

• Machine Learning，李宏毅，台大電機，2016. Convolutional Neural Network pdf, video