Week 1 (02/28) : Holiday - NO CLASS
Week 1 (02/28) : Holiday - NO CLASS
Embedded Files
228放假停課一次
Week 2 (03/06) : About This Course
Week 2 (03/06) : About This Course
Lecture Note : About this course (PPTX, PDF)
Homework : Create your web site
Readings :
十三分鐘略懂 AI 技術：機器學習、深度學習技術原理及延伸應用 Youtube 13'27", 2021
Which is Better For Your Machine Learning Task, OpenCV or TensorFlow?, 2021
機器學習案例：Alpha Go 2017
為何機器學習需要數學基礎：The Mathematics of Machine Learning
為何機器學習需要Python : Python、R、Java、 C++ ：從業界反饋看機器學習語言趨勢
Week 2 (03/13) : Introduction to Machine Learning
Week 2 (03/13) : Introduction to Machine Learning
Lecture Note : Introduction to Machine Learning (PPTX, PDF)
Homework : Python (I)
Readings :
Football Games Analysis from video stream with Machine Learning, 2021
Artificial Intelligence, Machine Learning, and Deep Learning — What the Difference? 2020
Top 10 Libraries In C/C++ For Machine Learning, Medium, 2020
Contemporary Classification of Machine Learning Techniques (Part 1), 2019
Machine Learning: A Primer - an introduction for both technical and non-technical readers, 2018
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
A Friendly Introduction to Machine Learning, Udacity 2016. Youtube video 30'52"
Chapter 1 Introduction, Introduction to Machine Learning, 2nd, E. Alpaydin, MIT Press, 2010
Week 3 (03/20) : ML for Classification
Week 3 (03/20) : ML for Classification
Lecture Note : Machine Learning for Pattern Recognition (PPTX, 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.
Week 4 (03/27) : ML Problems and Algorithms
Week 4 (03/27) : ML Problems and Algorithms
Lecture Note : Machine Learning Problems and Algorithms (PPTX, PDF)
Homework : Python (III)
Readings :
Part 1 Overview, Goals, Learning Types, and Algorithms , Machine Learning: An In-Depth, Non-Technical Guide. 2017.
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.
Week 6 (04/03) : Holiday - NO CLASS
Week 6 (04/03) : Holiday - NO CLASS
春假停課一次
Homework : P1 Scikit-Learn
Week 7 (04/10) : Supervised Learning
Week 7 (04/10) : Supervised Learning
Lecture Note : Supervised Learning (PPTX, PDF)
Homework : P2 Trees and Forests
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.
Predictive modeling, supervised machine learning, and pattern classification - the big picture, 2014
KNN (K-Nearest Neighbor)
Decision Tree
Implications of Information Theory in Machine Learning, 2021
Decision Tree in Machine Learning, Towards Data Science, 2018
SVM (Support Vector Machine)
支持向量机，维基百科。
Support Vector Machines (SVM) and the Multi-Dimensional Wizardry, 2022
Machine Learning Youtube channel, StatQuest with Josh Starmer, 2019
Support Vector Machine (SVM): A Visual Simple Explanation — Part 1, 2019
Support Vector Machine (SVM) pdf,video, Machine Learning課程，李宏毅，台大電機系，2016.
SVM, Xianxu Hou's blog, 2015
Week 8 (04/17) : Midterm - NO CLASS
Week 8 (04/17) : Midterm - NO CLASS
Homework : P3 kNN
Week 9 (04/24) : Linear Regression
Week 9 (04/24) : Linear Regression
Lecture Note : Linear Regression (PPTX, PDF)
Homework : Homework : P4 SVM
Readings :
A Beginner’s Guide to Regression Analysis in Machine Learning, 2021
Fundamentals of Linear Regression for Machine Learning, 2021
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.
Algorithms From Scratch: Linear Regression - Detailing and Building a Linear Regression model from scratch, Kurtis Pykes, 2020. Towards Data Science. (Scikit-Learn)
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.
Regression: Case Study, pdf, video, Machine Learning，李宏毅，台大電機，2016
Gradient descent
Gradient descent tutorials
Gradient descent implementation
Week 10 (05/01) : Logistic Regression
Week 10 (05/01) : Logistic Regression
Lecture Note : Logistic Regression (PPTX, PDF)
Homework : P5 Linear Regression
Readings :
Guide to an in-depth understanding of logistic regression, 2016
D.G. Kleinbaum and M. Klein, Logistic Regression: A Self-Learning Text, 3rd, 2010.
Chapter 5 Logistic regression, Machine Learning in Action, by Peter Harrington, Manning Publications Co., 2012 (PDF)
Breaking it Down: Logistic Regression, 2022
Exploring the fundamentals of logistic regression with NumPy, TensorFlow, and the UCI Heart Disease Dataset
Logistic Regression With Gradient Descent in Excel, 2020
So you can better understand how Logistic Regression works
Logistic Regression - A gentle introduction to Logistic Regression, Towards Data Science, 2020
Intuitively, How Can We (Better) Understand Logistic Regression, 2020
How To Implement Logistic Regression From Scratch in Python, 2019
👍 StatQuest: Logistic Regression, Youtube channel, StatQuest with Josh Starmer, 2018-2020
Machine Learning From Scratch: Logistic Regression. Classification Fundamentals in Python. 2018
Practical aspects — Logistic Regression in layman terms, 2018
Classification: Logistic Regression pdf, pptx, video, Machine Learning課程，李宏毅，台大電機，2017.
Week 11 (05/08) : Logistic Regression - Multi-class and Softmax
Week 11 (05/08) : Logistic Regression - Multi-class and Softmax
Lecture Note : Logistic Regression (PPTX, PDF)
Homework : P6 Logistic Regression
Readings :
Essential guide to Multi-Class and Multi-Output Algorithms in Python, 2021
Deep Learning Tutorial: Linear Regression, Logistic Regression, Softmax regression.
What is Softmax Regression and How is it Related to Logistic Regression? 2016
Softmax Regression, Logistic Regression, Stanford UFLDL(Unsupervised Feature Learning and Deep Learning) Tutorial
Week 12 (05/15) : Neural Information Processing
Week 12 (05/15) : Neural Information Processing
Lecture Note : Neural Information Processing (PPTX, PDF)
Homework : P7 Clustering
Readings :
Every Machine Learning Algorithm Can Be Represented as a Neural Network. The Algorithm of Algorithms. Medium, 2020
A Beginners Guide to Neural Nets - Towards Data Science, 2020
Neural Networks are Function Approximation Algorithms, by Jason Brownlee, 2020
A Comprehensive Guide on Activation Functions - Taking a modern tour over the many curves of data-science, 2020
Neural Network Activation Function Types - Understanding what really happens in a neural network, 2019
Understanding Neural Network Neurons - Explaining What These Smart Components Doing? 2019
The Perceptron — A Building Block of Neural Networks - AND HOW IT CAN COMPUTE LOGICAL STATEMENTS, 2019
Introduction To Artificial Intelligence — Neural Networks, 2019
Machine Learning for Beginners: An Introduction to Neural Networks, 2019
The differences between Artificial and Biological Neural Networks, 2018
Demystifying Neural Networks: A Mathematical Approach (Part 1), 2018
The Scuffle Between Two Algorithms -Neural Network vs. Support Vector Machine, 2018
Introduction to Artificial Neural Networks : Part 1, Part 2 - Learning, 2014
Machine Learning: An Algorithmic Perspective, S. Marsland, Chapman and Hall/CRC, 2009
Chapter 11 Multilayer Perceptrons, Introduction to Machine Learning, 2nd, E. Alpaydin, MIT Press, 2010 (PDF)
Week 13 (05/22) : Backpropagation
Week 13 (05/22) : Backpropagation
Lecture Note : Backpropagation (PPTX, PDF)
Homework : P8 PyTorch and Neueal Networks
Readings
Do You Understand Gradient Descent and Backpropagation? Most Don’t. 2020
Machine Learning - Neural Networks Tutorial, Paul Tero of Existor Ltd, 2015
Backpropagation in 5 Minutes, Youtube 5'28" 2017 (中文字幕)
Neural Networks, Youtube videos (中文字幕)
1. What is Neural Network (19’13")
2. How Neural Networks Learn (21’01")
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
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. Youtube 24'37". Blog. How neural networks work.
Readings Advanced
Why momentum really works, Distill, 2017
The Evolution of Gradient Descent, Youtube 9'18", 2017. (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)
Week 14 (05/29) : Deep Learning
Week 14 (05/29) : Deep Learning
Lecture Note : Deep Learning Introduction (PPTX, PDF)
Homework : P9 Learning of Neural Networks. Project Announcement
Readings
Neural network (backpropagation) playground by TensorFlow (1 hidden layer, activation=tanh, batch size=10)
Deep Learning State of the Art 2020, MIT course, Youtube video: 1:27:41, 2020
Intuitive Deep Learning : Introduction, 2019
Loss Functions Explained, 2019
Intro to Deep Learning - Neural networks for newbies, novices, and neophytes, 2019
What and why NN. CNN, RNN, applications Part 4: Neural Networks & Deep Learning, Machine Learning for Humans, by V. Maini, Medium, 2017
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。
Deep Learning for Computer Vision – Introduction to Convolution Neural Networks, 2016
Machine Learning，李宏毅，台大電機，2016
Readings Advanced
V. Kumar and M. L, “Deep Learning as a Frontier of Machine Learning: A Review,” International Journal of Computer Applications, vol. 182, no. 1, pp. 22–30, Jul. 2018
J. Schmidhuber, "Deep Learning in Neural Networks: An Overview," arXiv:1404.7828v4, 2014
Week 15 (06/05) : Convolution Neural Networks
Week 15 (06/05) : Convolution Neural Networks
Lecture Note : Convolutional Neural Network (CNN) (PPTX, PDF)
Homework : Project proposal
Readings :
【機器學習2021】卷積神經網路 (Convolutional Neural Networks, CNN)，台大李宏毅Youtube 2021. ML2021 week3 CNN slides. 55'38"
Convolutional Neural Networks Explained (What Is Computer Vision), Youtube 2020. 10'46"
Advanced Topics in Deep Convolutional Neural Networks, 2019
Neural Network 3D Simulation, Youtube 2’44"
Computer Vision: A Study On Different CNN Architectures and their Applications, 2019
A Gentle Introduction to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), 2019
A Gentle Introduction to Pooling Layers for Convolutional Neural Networks, 2019
Gentle Dive into Math Behind Convolutional Neural Networks - Mysteries of Neural Networks Part V, 2019
Intuitive Deep Learning : Introduction, 2019
Convolution: An Exploration of a Familiar Operator’s Deeper Roots, 2018
Convolutional Neural Networks for Beginners - Practical Guide with Python and Keras, 2018
(ReLU) An Overview to Vanishing Gradient Problem, 2018
Ted’s Lab, 2018
Tutorial: Derivation of Convolutional Neural Network from Fully Connected Network, 2018
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. Youtube 26'13". Blog.
Convolutional Neural Network pdf, video, Machine Learning課程，李宏毅，台大電機，2016
Week 16 (06/12) : Final Term - NO CLASS
Week 16 (06/12) : Final Term - NO CLASS
Week 17 (06/19) : 彈性多元學習週 ─ Project oral presentation
Week 17 (06/19) : 彈性多元學習週 ─ Project oral presentation
Week 18 (06/26) : 彈性多元學習週 ─ Project report
Week 18 (06/26) : 彈性多元學習週 ─ Project report
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