I5840A 教學大綱表

教學大綱表 (110學年度第2學期)

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課程名稱 Course Title	(中文) 自然語言處理 (英文) Natural Language Processing			開課單位 Departments	資訊工程研究所
				課程代碼 Course No.	I5840A
授課教師 Instructor	葉慶隆
學分數 Credit	3.0	必/選修 core required/optional	選修	開課年級 Level	研究所
先修科目或先備能力(Course Pre-requisites)：程式設計、基礎數學(微積分、機率、離散數學)
課程概述與目標(Course Overview and Goals)：自然語言處理和電腦視覺是人工智慧做為智慧型代理人(Intelligent Agent)的溝通及感知的主要能力，資料驅動的深度學習技術是促成電腦這些能力的重要基礎。近來深度學習技術已經成為實現溝通及感知的基礎建設，並且廣泛整合在課程中。一直以來人工智慧是電腦科學裡最複雜的領域之一，學生需要相當紮實的資訊及數學基礎，才能學好這門課。這門課將針對只具備基本程式設計能力，及基礎數學的學生，以問題導向為主軸，結合各種深度學習技術，引領學生認識自然語言處理。本課程將依以下順序進行：自然語言處理介紹基礎字串處理：Regular Expressions, Text Normalization, Edit Distance 以邏輯斯分類(logistic classification)為基礎的情感分析(sentiment analysis) 以Naive Bayes 為基礎的情感分析向量空間模型向量語意及詞嵌入(Vector Semantics and Word Embedding) 以神經網路為基礎的情感分析遞迴神經網路(RNN)和語言模型(language modeling) 序列對序列神經網路及機器翻譯自然語言處理與關注模型(NLP and Attention Model)
教科書(Textbook)	Dan Jurafsky and James H. Martin, Speech and Language Processing, 3rd ed.
參考教材(Reference)	Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016 Tensorflow Tutorial, tensorflow.org Charles Severance, Python for Everybody Introduction to AI, udacity.com AI for Everyone, coursera.org Introduction to TensorFlow for Deep Learning, udacity.com

課程大綱 Syllabus			學生學習目標 Learning Objectives	單元學習活動 Learning Activities	學習成效評量 Evaluation	備註 Notes
序 No.	單元主題 Unit topic	內容綱要 Content summary	學生學習目標 Learning Objectives	單元學習活動 Learning Activities	學習成效評量 Evaluation	備註 Notes
1	Welcome and introduction to Natural Language Processing	NLP background Knowledge in Speech and Language Processing Ambiguity Models and Algorithms Language, Thought, and Understanding Some Brief History	NLP basis
2	Regular Expressions, Text Normalization, Edit Distance (1)	Regular Expressions Word and Corpora Word Tokenization Word Normalization	Regular Expressions, Text Normalization
3	Regular Expressions, Text Normalization, Edit Distance (2)	Python RE Python NLTK	Python programming: re and nltk
4	Sentiment Analysis Using Logistic Classification(1)	Supervised ML and Sentiment Analysis Vocabulary & Feature Extraction Negative and Positive Frequencies Feature Extraction with Frequencies	Sentiment Analysis Using Logistic Classification
5	Sentiment Analysis Using Logistic Classification(2)	Preprocessing Putting it All Together Logistic Regression Overview Logistic Regression: Training Logistic Regression: Testing	Sentiment Analysis Using Logistic Classification			專題分組開始。專題第一階段：python re, nltk; Sentiment Analysis using Logistic Classification
6	Sentiment Analysis with Naïve Bayes (1)	Probability and Bayes’ Rule Naïve Bayes Introduction Laplacian Smoothing Log-Likelihood	Sentiment Analysis with Naïve Bayes
7	Sentiment Analysis with Naïve Bayes (2)	Training Naïve Bayes Testing Naïve Bayes Applications of Naïve Bayes Naïve Bayes Assumptions	Sentiment Analysis with Naïve Bayes			專題分組開始。專題第二階段：Sentiment Analysis using Naive Bayes Method
8	Vector Space Models	Intro. to Vector Space Models Word by Word and Word by Doc. Euclidean Distance Cosine Similarity Manipulating Words in Vector Spaces Visualization and PCA	Vector Space Models
9	期中考週	期中測驗	期中測驗
10	Vector Semantics and Embeddings (1)	Overview Basic word Representation Word Embeddings How to Create Word Embeddings Word Embedding Methods Continuous Bag-of-Words Model	Vector Semantics and Embeddings
11	Vector Semantics and Embeddings (2)	Cleaning and Tokenization Sliding Window of Words in Python Transforming Words into Vectors Architecture of the CBOW Model Architecture of the CBOW Model: Activation Functions	Vector Semantics and Embeddings
12	Vector Semantics and Embeddings (3)	Training a CBOW Model Extracting Word Embedding Vectors Evaluating Word Embeddings Examples of Word Embedding	Vector Semantics and Embeddings
13	Neural Networks for Sentiment Analysis	Neural Networks and Forward Propagation Trax: Neural Networks Trax: Layers Dense and ReLU Layers Serial Layer Other Layers Traiining	Neural Networks for Sentiment Analysis
14	Recurrent Neural Networks for Language Modeling	Traditional Language Models Recurrent Neural Networks Applications of RNNs Math in Simple RNNs Cost Function for RNNs Gated Recurrent Units	Recurrent Neural Networks for Language Modeling
15	Neural Machine Translation and Transformer	Seq2Seq Alignment Attention Setup for Machine Translation Introduction to Transformer	Neural Machine Translation and Transformer
16	期末報告 (1)	期末報告	期末報告
17	期末報告(2)	期末報告	期末報告
18	期末考週	期末測驗	期末測驗

教學要點概述：
1.自編教材 Handout by Instructor：
□ 1-1.簡報 Slids
□ 1-2.影音教材 Videos
□ 1-3.教具 Teaching Aids
□ 1-4.教科書 Textbook
□ 1-5.其他 Other
□ 2.自編評量工具/量表 Educational Assessment
□ 3.教科書作者提供 Textbook

成績考核 Performance Evaluation：：20% 作業：30% ：30% 專題：20%

教學資源(Teaching Resources)：
□ 教材電子檔(Soft Copy of the Handout or the Textbook)
□ 課程網站(Website)
扣考規定：https://curri.ttu.edu.tw/p/412-1033-1254.php