B5312 - Programming for Language Studies (LM)

Learning outcomes

At the end of the course, the student has competences on theoretical and practical foundations for the acquisition, manipulation and analysis of text and data using computational tools. Furthermore, the student will be familiar with the methodological foundations for the development of scripts for natural language processing. They know and use the fundamental algorithms and data structures and are able to build and interpret graphs that show descriptive statistics of the data collected in order to facilitate its analysis.

Course contents

The course consists of theoretical lessons and practical sessions. In each lesson, after a theoretical introduction, a practical session will take place in which the student will be asked to experience the introduced topic first-hand.

The course is organised in two modules. The first module covers basic programming concepts, the second module covers advanced topics.

Below are the topics of the lectures:

Introduction to programming

• Algorithms
  
• Data structures
• Programming languages

Introduction to the Python language

• Python interpreter
• Jupyter Notebook
• Command Line interface
• Tools for managing virtual environments and dependecies (e.g. anaconda, virtualenv)
• Objects, Expressions, Data Types
• Python IDE

 Importing and Exporting data and text in Python

• How to import data and text into memory using a Python script
• Exporting data and text to files
  

Manipulating data and text in Python

• Natural language processing with NLTK

Describing and visualising data in Python

• Matplotlib

Libraries for Machine Learning

• Scikit-learn, Pytorch and tensorflow
  

Readings/Bibliography

• Downey, Allen. Think python (3rd ed). O'Reilly Media Link

• John V. Guttag. Introduction to Computation and Programming Using Python (2 ed.). The MIT Press.

Teaching methods

The teaching method is based on sessions of 2 to 4 hours each, which include interactive lectures and exercises to be carried out in class under the teacher's supervision.

Assessment methods

Two homeworks will be assigned that must be completed by the student before the examination.

The first of these homeworks will be assigned at the end of October, the second at the end of November.

These homeworks are not graded, but must be completed before the examination.

By the end of the course, the lecturer will agree the project specifications with the student.

The student will carry out the project at home and once completed will present the work done to the lecturer.

Teaching tools

In addition to the tools available in the classroom, students will use software components that are freely available online. The software will allow students to experience first-hand the concepts they learn in the classroom.

Office hours

See the website of Luigi Asprino