End-of-term exam

• Each student will be randomly assigned 2 topics, one about NLP and one about Python.

• The main focus will be on the NLP topic, but sufficient knowledge (at least that equivalent to a passing grade) must be demonstrated in both topics to pass the exam.

Online examination

Due to the current situation, all exams will be held on Microsoft Teams. This means:

• there is no preparation time after you are assigned topics,
• you must have your camera turned on,
• you must be prepared to share your screen and start writing code. We recommend that you already have Jupyter or Colab running when you start the exam.

Exam times and location

Date	Time	Location
May 25 (Tue)	10:15	Teams
Jun 01 (Tue)	10:15	Teams
Jun 08 (Tue)	10:15	Teams

I. NLP topics

1. Intro to NLP

• NLP tasks
  • Tokenization, lemmatization, POS tagging, language modelling
• Text representations
  • One hot encoding, TF-IDF
• Feature vectors, classification pipeline, logistic regression

2. Embeddings

• Problems with one-hot encoding
• Creating word embeddings, cosine similarity of word vectors
• Using word embeddings in NLP tasks

3. Deep Learning for NLP

• Learning types (supervised, unsupervised, classification, regression, clustering)
• Constructing train, validation and test sets
• Terminology
  • Loss function
  • Batch size
  • Epoch
  • Learning rate
• Feed forward neural networks
  • Neurons, activation functions, softmax
• Difference between feed forward and recurrent neural networks

4. Sequence modeling

• 3 basic types
  • Example applications
• What are the sequence elements? Pros and cons.
• Padding and batching

5. Transformers

• Attention
  • Basic idea not the exact formulation
• Transformer
  • Motivation
  • Basic components
  • Positional encoding

6. BERT

• Contextualized embeddings
• BERT tokenization
• BERT components
• Finetuning
• Applications

7. Evaluation metrics on NLP tasks

• Why do we use other metrics than accuracy?
• Evaluation of universal dependancy trees
• F-score
• ROUGE

8. Universal Dependecies

• What is in a dependency tree?
• What is CoNLL-U?
• Learning dependency parsing
  • Basic ideas, not step-by-step

II. Python topics

1. Introduction

• What is Jupyter?
• Cell types, cell magic
• Kernel
• Short history of Python
• Python community, PEP, Pythonista

2. Functions and generators

• args, kwargs
• Default arguments
• Lambda functions
• Generators, yield statement

3. Type system

• Static vs. dynamic typing
• Built-in types (numeric, boolean), operators
• Mutability

4. Sequence types

• list vs. tuple
• Operators
• Advanced indexing
• Extra: time complexity of basic operations (lookup, insert, remove, append etc.)
• Set type and operations

5. Strings

• Character encodings: Unicode vs. UTF-8, encoding, decoding
• Common string operations
• String formatting (mention at least two kinds)

6. Object oriented programming I.

• Data attributes, methods, class attirbutes
• Inheritance, super
• Duck typing
• Magic methods, operator overloading

7. Object oriented programming II.

• Assignment, shallow copy, deep copy
• Object introspection
• Class decorators, static methods, class methods
• Properties

8. List comprehension

• Basic list comprehension (you should be able to write one)
• Generator expressions
• Extra: iteration protocol, writing an iterator class
• Set and dict comprehension
• yield keyword

9. Exception handling

• Motivation
• Basic keywords
• Defining exception classes

10. Context managers

• Motivation
• Basic usage
• Defining context managers

11. Decorators

• What are decorators?
• @wraps decorator
• Decorators with parameters
• Classes as decorators

12. Functional Python

• map, filter, reduce

13. numpy

• ndarray
  • Defining ndarrays (mention at least 3 functions)
  • array attributes
• Indexing and advanced indexing
• Operations an arrays
  • Mention at least 5 operations
• Extra: broadcasting