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10702 工程
計算機程式設計一(資工版)
陳煥宗

This course is aimed to help the students learn how to program in C. There will be several labs, two midterm exams, one final exam, and the final project, with the following percentages......

 

 Course keywords  
C 程式設計, C programming, problem solving, basic data structures, preliminary algorithms.This course is aimed to help the students learn how to program in C. 

  

 Syllabus  
There will be several labs, two midterm exams, one final exam, and the final project,with the following percentages:  
 1  Online judge labs (20%)  - every two weeks 
 2  Two midterm exams (30%) 
 3  One final exam (30%) 
 4  Final project (20%) 
 
 
 
 
Text Books 
* S. Prata, C PRIMER PLUS 
* Lecture notes 
 
 
  
 Previous Course Webpage  
 

 

10702 工程
深度學習
吳尚鴻
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.

 
 
 
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
Competition 01
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
  

 

10702 工程
Web程式設計、技術與應用
吳尚鴻

This course gives a comprehensive, self-contained, and up-to-date introduction to the web/app development. We focus on the development challenges in real-world situations and present guidelines, tools, and best practices. Students are asked to team up and build real, useful applications (websites and/or mobile apps) accessible to the public in the end.

 

[Description]

The classes are divided into three parts. First, we give a primer to web fundamentals such as HTTP, HTML, CSS, and Javascript. We cover different programming paradigms, including the OOP and functional programming. Handy tools such as Git are covered to get students familiar with the project-based and team-based development. In the second part, we introduce modern web development techniques such as responsive design, Bootstrap, ES6/7, React, and Redux. Last, we extend our horizon to the backend and mobile development landscapes by introducing the Node.js, PostgreSQL database system, Amazon Web Services (AWS), and React Native. We also give case studies on how to leverage Machine Learning algorithms to convert raw user data into the AI.

 
 

[Syllabus]
 
Lecture 01

HTTP&HTML

Lecture 02

CSS

Lecture 03

Bootstrap and Responsive Design

Lecture 04

Javascript & DOM

Lecture 05

Modern Javascript


 

[Reference Books]

  • Alexander Osterwalder, Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, 2010

  • Eric Ries, The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses, 2011

  • Peter Thiel, Blake Masters, Zero to One: Notes on Startups, or How to Build the Future, 2014


 

[Online Courses]

 

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