Assignments will be posted on this page. Please note that to receive the starter code and submit the assignments, you will need to access the private course repository at https://github.com/snlp2020/snlp, which you will gain access after completing the first assignment If you cannot access the above URL (and if you are a student of this course) after the first week of the semester, please contact the instructor.

Assignments

• Assignment 0: introduction, warm up
• Assignment 1: corpus collection
• Assignment 2: regression and regularization
• Assignment 3: classification and cross validation
• Assignment 4: unsupervised learning
• Assignment 5: neural networks
• Assignment 6: sequence labeling
• Assignment 7: Sequence to sequence networks

Assignment policy

Assignments constitute 60% of the grade you will receive from this course (40% comes from your exam score). There will be 7 graded assignments, of which, 6 assignments (with best scores) will count toward your final grade. Assignments can be done in pairs. However, the same pair can work together only on a single assignment.

Assignments are only accepted through the GitHub repositories. No other form of submissions (e.g., email) are accepted.

The following information should be provided as the file/module docstring in Python assignments, and as comments at the beginning of the file for other file types that allow comments.

"""
 Course:      Statistical Language processing - SS2020
 Assignment:  (Enter the assignment, "lab 1, exercise 1", for example)
 Author(s):   (Enter the full names of author(s) here)
 Description: (Enter a brief description of the module,
               not needed for files provided as starter code)
 
 Honor Code:  I pledge that this program represents my own work.
"""