清華大學 / 教學發展中心 / 開放式課程 / English Version

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This class introduces the concepts and practices of deep learning. The course consists of three parts. In the first part, we give a quick introduction of classical machine learning and review some key concepts required to understand deep learning.In the second part......
 
  

Description
This class introduces the concepts and practices of deep learning. The course consists of three parts. In the first part, we give a quick introduction of classical machine learning and review some key concepts required to understand deep learning.In the second part, we discuss how deep learning differs from classical machine learning and explain why it is effective in dealing with complex problems such as the image and natural language processing. Various CNN and RNN models will becovered. In the third part, we introduce the deep reinforcement learning and its applications.This course also gives coding labs. We will use Python 3 as the main programming language throughout the course. Some popular machine learning libraries such as Scikit-learn and Tensorflow will be used and explained in detials.

※https://nthu-datalab.github.io/ml/index.html
 
 
 
Syllabus  
Lecture 01  
 Introduction/Scientific Python 101
Lecture 02     
 Linear Algebra/Data Exploration & PCA
Lecture 03
 Probability & Information Theory/Decision Trees & Random Forest

Lecture 04

 Numerical Optimization/Perceptron & Adaline/Regression
Lecture 05
 Learning Theory & Regularization /Regularization
Lecture 06
 Probabilistic Models/Logistic Regression & Metrics 
Lecture 07
 Non-Parametric Methods & SVMs/SVMs & Scikit-Learn Pipelines
Lecture 08
 Cross Validation & Ensembling/CV & Ensembling

Competition01  

 Predicting Appropriate Response
Lecture 09
 Large-Scale Machine Learning
Lecture 10
 Neural Networks: Design/TensorFlow101 & Word2Vec
Lecture 11
 Neural Networks: Optimization & Regularization
Lecture 12

 Convolutional Neural Networks/Nuts and Bolts of Convolutional Neural Networks/Visualization and Style Transfer

Competition 02

 Image Object Detection & Localization

Lecture 13

 Recurrent Neural Networks/Seq2Seq Learning for Machine Translation
Competition 03 Image Caption
Lecture 14
 Unsupervised Learning/Autoencoders/GANs
Competition 04 Reverse Image Caption 
Lecture 15
 Semisupervised/Transfer Learning and the Future
Lecture 16
 Reinforcement Learning/Q-learning
Lecture 17
 Deep Reinforcement Learning/ DQN & Policy Network
Competition 05 You Draw I Draw
   
 

 

Reference Books 
♠ Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016, ISBN: 0387848576
♠ Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer, 2009, ISBN: 0387848576
♠ Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006, ISBN: 0387310738
♠ Sebastian Raschka, Python Machine Learning, Packt Publishing, 2015, ISBN: 1783555130
  
 
 
 
Online Courses 
♠ CS231n: Convolutional Neural Networks for Visual Recognition, Stanford
♠ CS224d: Deep Learning for Natural Language Processing, Stanford
♠ CS 294: Deep Reinforcement Learning, Berkeley
♠ MIT 6.S094: Deep Learning for Self-Driving Cars, MIT
   

關鍵字

深度學習,Deep Learning,Scientific Python,Neural Networks,Numerical Optimization

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