This course on Machine Learning aims to introduce learners to the fundamentals of machine learning, including regression, classification, deep learning, and various unsupervised learning techniques. By the end of the course, students will be able to understand and apply gradient descent, logistic regression, convolutional neural networks, support vector machines, and reinforcement learning. The teaching method includes lectures, demos, case studies, and practical tips for training neural networks. This course is intended for individuals interested in gaining a comprehensive understanding of machine learning concepts and techniques, regardless of their prior experience in the field.
Overview
Syllabus
ML Lecture 0-1: Introduction of Machine Learning.
ML Lecture 0-2: Why we need to learn machine learning?.
ML Lecture 1: Regression - Case Study.
ML Lecture 1: Regression - Demo.
ML Lecture 2: Where does the error come from?.
ML Lecture 3-1: Gradient Descent.
ML Lecture 3-2: Gradient Descent (Demo by AOE).
ML Lecture 3-3: Gradient Descent (Demo by Minecraft).
ML Lecture 4: Classification.
ML Lecture 5: Logistic Regression.
ML Lecture 6: Brief Introduction of Deep Learning.
ML Lecture 7: Backpropagation.
ML Lecture 8-1: “Hello world” of deep learning.
ML Lecture 8-2: Keras 2.0.
ML Lecture 8-3: Keras Demo.
ML Lecture 9-1: Tips for Training DNN.
ML Lecture 9-2: Keras Demo 2.
ML Lecture 9-3: Fizz Buzz in Tensorflow (sequel).
ML Lecture 10: Convolutional Neural Network.
ML Lecture 11: Why Deep?.
ML Lecture 12: Semi-supervised.
ML Lecture 13: Unsupervised Learning - Linear Methods.
ML Lecture 14: Unsupervised Learning - Word Embedding.
ML Lecture 15: Unsupervised Learning - Neighbor Embedding.
ML Lecture 16: Unsupervised Learning - Auto-encoder.
ML Lecture 17: Unsupervised Learning - Deep Generative Model (Part I).
ML Lecture 18: Unsupervised Learning - Deep Generative Model (Part II).
ML Lecture 19: Transfer Learning.
ML Lecture 20: Support Vector Machine (SVM).
ML Lecture 21-1: Recurrent Neural Network (Part I).
ML Lecture 21-2: Recurrent Neural Network (Part II).
ML Lecture 22: Ensemble.
ML Lecture 23-1: Deep Reinforcement Learning.
ML Lecture 23-2: Policy Gradient (Supplementary Explanation).
ML Lecture 23-3: Reinforcement Learning (including Q-learning).
ML Lecture 21-1: Recurrent Neural Network (Part I) English version.
Taught by
Hung-yi Lee
