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課程名稱 |
統計與大氣科學
Statistics with Meteorological Applications |
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開課學期 |
110-2 |
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授課對象 |
理學院 大氣科學系 |
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授課教師 |
羅敏輝 |
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課號 |
AtmSci2019 |
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課程識別碼 |
209 22210 |
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班次 |
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學分 |
2.0 |
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全/半年 |
半年 |
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必/選修 |
必修 |
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上課時間 |
星期二3,4(10:20~12:10) |
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上課地點 |
大氣系A100 |
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備註 |
與梁禹喬合授
總人數上限：45人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
Data statistical analysis is essential to research and applications in atmospheric/climate Sciences.
Students of this course will learn step by step various theories and methods of basic data statistical analysis which usually be applied in atmospheric sciences.
Assignments:
HWs are due every Saturday night at 22:00, and everyone needs to finish the HWs. You can discuss with your classmates/friends, and no plagiarize!
Project: Form a team (two people) to solve a self-selected problem. |
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課程目標 |
introduce the probability concept, probability distributions, fundamental statistical approach and applications in the atmospheric and climate sciences. |
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課程要求 |
Homework:
Students will be asked to use Matlab/Python to finish the HWs.
HWs are due every Saturday night at 10pm.
There are one mid-term and final exam.
Final project:
Form a team (with 2 people) to solve a self-selected problem.
Each team does an oral presentation and make a movie for 7~10 mins.
本課程有FACEBOOK社團（2019 NTU/AS Statistics with Meteorological Applications）(TBD) |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
待補 |
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參考書目 |
Wilks. D.S., 2011: Statistical Methods in the Atmospheric Sciences
(http://www.sciencedirect.com/science/bookseries/00746142/100)
Michael Nielsen 2019: Neural Networks and Deep Learning
http://neuralnetworksanddeeplearning.com/index.html |
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評量方式
(僅供參考) |
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週次 |
日期 |
單元主題 |
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第1週 |
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Introduction to statistics and data analysis in Atmospheric and climate sciences |
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第2週 |
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population and sample; expectation, variance |
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第3週 |
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sample variance and sampling |
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第4週 |
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estimation |
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第5週 |
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Estimation |
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第6週 |
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Lecture 9: Regression
you can download the ppt here: https://drive.google.com/file/d/1fEoQF4d3m7V3LWnlPiUPMDcoqxmqXLlo/view
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第7週 |
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No class |
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第8週 |
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Midterm |
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第9週 |
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Hypothesis testing (1) |
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第10週 |
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Hypothesis testing (2) |
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第11週 |
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Hypothesis testing (3) |
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第12週 |
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Regression analysis (1) |
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第13週 |
5/10 |
Deep learning and neural network |
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第14週 |
5/17 |
Improving neural network |
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第15週 |
5/24 |
convolutional neural network
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第16週 |
5/31 |
final exam |
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第17週 |
6/7 |
final project |
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