校際選修

115-1 選課時程

進行中

  • 初選第一階段 6/15/2026
  • 初選第二階段 6/22/2026
  • 校際選修 8/24/2026
  • 初選第三階段 8/31/2026
  • 開學後加退選 9/7/2026
  • 逾期加退選 9/21/2026
選課資源

雲端運算與巨量資料分析

Cloud Computing and Big Data Analytics

學期
110-2
學分
3 學分
當期課號
5052
永久課號
GEE9022
開課單位
電機工程學系
授課教師
帥宏翰
校區
光復
類別
選修
上課時間表
週四
2
09:00–09:50
雲端運算與巨量資料分析
EDB01
3 節連堂
3
10:10–11:00
4
11:10–12:00

* 根據陽明交大上課時間表所列

概述

See AI everywhere and want to be a master of AI? Need to incorporate data-driven decisions learned from big data into your research? This course provides an overview of machine learning techniques for different kinds of data. You will be introduced to models and algorithms you can use to create machine learning models and to scale those models up on cloud platform to big data problems. At the end of the course, you will be able to: o Design a machine learning approach to leverage the knowledge from data. o Apply machine learning techniques to your own applications. o Know about the state-of-the-art approaches for natural language processing and computer vision. o Analyze big data problems using scalable machine learning algorithms on Spark. o Use WISE-PaaS for building your AIOT applications.

先修科目

Python

評分方式

Lab (9%): 3% each for Labs 1-3 Homework (42%): 10% for HW#1, 8% each for HW #2, #3, #4 and #5 Midterm (20%) Final project (29%) Bonus (up to 6 pts): class participation

週次計畫
週次主題
第 0 週Anomaly Detection (Homework 3)
第 0 週Basic Tools (DNN, CNN and RNN)-I (Homework 1)
第 0 週Basic Tools (DNN, CNN and RNN)-II
第 0 週Causality
第 0 週Course Introduction
第 0 週Distributed Algorithm
第 0 週Final Project Preparation
第 0 週Final Project Presentation
第 0 週Generative Adversarial Networks
第 0 週Hadoop and MapReduce
第 0 週Introduction to Cloud Computing and WISE-PaaS (Homework 4)
第 0 週Large-scale Machine Learning and Scalable Algorithm
第 0 週Meta Learning
第 0 週Midterm
第 0 週Philosophy and Introduction to Deep Learning
第 0 週Reinforcement Learning
第 0 週Self-supervised Learning (Homework 2)
第 0 週Spark (HW #5)
教科書

1. Deep Learning with PyTorch by Eli Stevens, Luca Antiga and Thomas Viehmann 2. Data Mining by Aggarwal, Charu C. 3. Learning Spark by Holden Karau, Andy Knowinski, Patrick Wendell & Matei Zaharia

Office Hours
地點
ED-807
時間
TA hour: CD, Wednesday or by appointment @ ED-716 •Wei-Lun Eric Tseng: eric840610.ee02@g2.nctu.edu.tw Office hour: 2 pm - 4 pm (every Tuesday)
聯絡方式
TEL: (03)571-2121#54530 EMAIL: hhshuai@nctu.edu.tw