一、課程說明、教學進度(Course Desc<x>ription)(Syllabus) 
<br>
<br>Week 1: Introduction + jupyter nb, environment setup, github
<br>Week 2: Ngram model + Skip Bigram + word em<x>bedding (word2vec) (word 
<br>representation) 
<br>Week 3: cLang 8 + BNC cross corpora analysis
<br>Week 4: html Parser 
<br>Week 5: collocation / spacy
<br>Week 6: streamlit, Final expectation
<br>Week 7: ML general topic (masked LM?) + potential final topics
<br>Week 8: Final Topic Proposal
<br>Week 9: 文步分類 (sequence labeling)
<br>Week 10: Grammar Patterns Tagging 
<br>Week 11: Final Project Progress Check
<br>Week 12: GEC (sequence to sequence)
<br>Week 13: senseNet (autoencoding)
<br>Week 14: Final Project Progress Check
<br>Week 15: TBD
<br>Week 16: Final Presentation 1
<br>Week 17: Final Presentation 2
<br>Week 18: 期末考週
<br>
<br>二、指定用書(Text Books)
<br>
<br>1. Natural Language Processing with Python –- Analyzing Text with the Natural 
<br>Language Toolkit, by Steven Bird, Ewan Klein, and Edward Loper 
<br>(http://www.nltk.org/book/)
<br>
<br>三、參考書籍(References)
<br>
<br>1. Keras Tutorial: Deep Learning in Python (https://goo.gl/6P6zLH)
<br>2. A Word2Vec Keras tutorial
<br>(adventuresinmachinelearning.com/word2vec-keras-tutorial/)
<br>
<br>
<br>四、教學方式(Teaching Method)
<br>
<br>1. Lectures
<br>2. In-class and take-home exercises
<br>
<br>
<br>五、成績考核(Evaluation)
<br>
<br>Assignments
<br>Midterm exam (coding)
<br>Term project
<br>
<br>