Skip to content

Material for the 2017 fall edition of the course "Introduction to Python and Human Language Technologies" at BME AUT

License

Notifications You must be signed in to change notification settings

peternagy1332/python_nlp_2017_fall

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

40 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Introduction to Python and Human Language Technologies

Time and place

Lecture: each Wednesday, 12.15 pm. - 2.00 pm. at Q.BF14, starting on September 5th Lab: each Friday, 12.15 pm. - 2.00 pm. at Q.B237, starting on September 7th

Instructors

  • Judit Acs
  • Gabor Borbely
  • David Nemeskey
  • Gabor Recski

Administrative and technical questions should be directed to Gabor Recski (recski AT aut DOT bme DOT hu)

Official course syllabus and requirements (TAD)

Schedule

Week Lecture Lab
1 9/6/2017 Intro + Python Intro 9/8/2017 Jupyter, basic exercises
2 9/13/2017 Built-in types, functions 9/15/2017 intermediate exercises
3 9/20/2017 NO CLASS (Rektori szünet) 9/22/2017 more complex exercises
4 9/27/2017 Object oriented Python, modules 9/29/2017 complex OOP exercises HW 1
5 10/4/2017 generators, context managers, CLI 10/6/2017 advanced exercises
6 10/11/2017 decorators, packaging 10/13/2017 numpy, scipy
7 10/18/2017 Tagging problems 10/20/2017 numpy, scipy Deadline - HW 1
8 10/25/2017 Morphology 10/27/2017 Tagging problems HW 2
9 11/1/2017 NO CLASS (All Saints' Day) 11/3/2017 Morphology
10 11/8/2017 Syntax 11/10/2017 Syntax
11 11/15/2017 Translation 11/17/2017 Translation HW 3 / Deadline - HW 2
12 11/22/2017 Semantics 1 11/24/2017 NO CLASS (Open House)
13 11/29/2017 Semantics 2 12/1/2017 Semantics Lab 1
14 12/6/2017 Semantics 3 12/8/2017 Semantics Lab 2 Deadline - HW 3

About

Material for the 2017 fall edition of the course "Introduction to Python and Human Language Technologies" at BME AUT

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 61.9%
  • HTML 38.0%
  • Python 0.1%