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歡迎踴躍選修英文短期課程-106/7/10(一)-7/15(六) 西交利物浦大學Dr. Kevin Kam Fung Yuen(袁錦鋒),2學分

各位同學,

電子系擬於106/7/10()-7/15()安排短期密集課程:ET5922701電子工程專論()2學分。上課相關資料如下。有意選修者,請於7/3()填妥附之選課單請指導教授簽名後繳至EE-404或掃瞄回傳。謝謝!

Dear students,

Our department intends to open an intensive short-term course. The course description is as follows:

Course Code & Name: ET5922701電子工程專論()  (Special Topics on Electronic Engineering (II))

Title: Machine Learning Algorithms (學習算法)

Instructor: Dr. Kevin Kam Fung Yuen, Associate Professor, Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China.

Class dates & Class hours:

10th – 15th July

9:10-12:10 (2,3,4) & 13:20- 16:20 (6,7,8)

Duration: 36 hours

Credit: 2

Course language: English

Class Room10th - 14th July  IB-608

                                    15th July  T3-301

 

Objective:

Machine learning is the science that provides computers with the ability to learn from data and produce meaningful patterns and conclusions without being explicitly programmed. This course will deliver the popular machine learning techniques. Topics include: (i) Supervised learning (regression, neural networks, decision trees, nearest neighbours); (ii) Unsupervised learning (hierarchical clustering, k-Means clustering); (iii) Reinforcement learning (Markov decision process, Q-Learning); (iv) Evolutionary learning (Genetic Algorithm); (v) R programing. During the labs and tutorials, students can learn the practical R code for each machine learning algorithm to deal with the real world applications. Students will learn the practical knowledge in the basic principles, techniques, algorithms, implementations and applications of Machine Learning.

References:

1.    Stephen Marsland, Machine Learning: An algorithmic Perspective, 2nd edition, CRC, 2014

2.    James, G., Witten, D., Hastie, T., Tibshirani, R, An Introduction to Statistical Learning : with Applications in R, Springer, 2013

3.    Pace, L., Beginning R:An Introduction to Statistical Programming, apress, 2012

Prerequisite: Calculus, Linear algebra, Probability, Basic computer programming

Outline:

1.      (3 hours) Introduction and preliminaries

2.      (3 hours) R programming

3.      (3 hours) Regression

4.      (3 hours) Statistical analysis for feature selection

5.      (3 hours) Genetic algorithm

6.      (3 hours) k-nearest neighbours (Assignment 1)

7.      (3 hours) Perceptron and neural networks

8.      (3 hours) ID3 and C5.0

9.      (3 hours) K-means

10.    (3 hours) Hierarchical clustering

11.    (3 hours) Markov decision process

12.    (3 hours) Q-Learning (Assignment 2)

Grading:

Assignment 1: Group (50%)

Assignment 2: Individual (50%)

 

 

 

電子系短期密集課程人工選課

 

(English Short Course Application Form)

 

選課日期:即日起10673  (Deadline: 3rd July 2017)

 

系所、班別

Department

  

Name

   

Student ID

聯絡電話

Telephone No.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

指導教授簽名

(Advisor's signature)

 

-------------------------------------------------------------------------------------

課程資料:

課程名稱

Course Name and Title

學分

Credit

授課教授

Instructor

上課時間

Class dates & Class hours:

教室

Room

ET5922701電子工程專論()

[Title]

Machine Learning Algorithms

(學習算法)

2

Dr. Kevin Kam Fung Yuen

10th July(Mon.)~15th July(Sat.), 09:10- 12:10 (2,3,4) &

13:20- 16:20 (6,7,8)/every day

(合計36小時)

10th - 14th July  IB-608

15th July  T3-301

選課期間自即日起至106/7/3(),請填妥簽名後繳至EE-404或掃瞄回傳 huang1208@mail.ntust.edu.tw 黃雪芬小姐收。

The application deadline is 3rd July 2017, Please submit to EE-404 or mail to huang1208@mail.ntust.edu.tw Ms. Huang.

 

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