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# # Open Items Lecture 2
# 
# * Fixed [shirtless slide](/notebooks/chapters/introduction.ipynb#/slide-9-0)!
# * Workload for assignments: 5h-20h per assignment, depending on your background, skills etc
# * Module Selection Priorities:
#     * Optional
#     * Elective
#     * Non-CS (update: will not be impossible)
# * First Come First Serve, but Elective students will only know if they have a spot after next Friday

# # Open Items Lecture 3
# 
# * Please use the moodle for questions regarding the assignment
# * No office hour today
# * Next: [Language Models: MLE](chapters/language_models_slides.ipynb)

# 
# # Open Items Lecture 4
# 
# * Assignment questions?
#     * Please use the moodle for questions regarding the assignment
# * I will not be available for office hours today
# * Next: [Language Models: MLE](chapters/language_models_slides.ipynb)

# 
# # Open Items Lecture 5
# 
# * Assignment Questions
#     * Are we allowed to use the `replace_OOVs` function on `_snlp_test_song_words`?
#     * Will the hidden test set we are assessed on already have the replace OOV's function applied to it? 
#     * Can we use helper functions from the exercices in the coursework?
#     * Question on the `OOVAwareLM`
# * Next: Word-Based Machine Translation

# # Open Items Lecture 6
# 
# * Do read the course notes
# * Assignment:
#     * You are allowed to use both development and training set as "larger training set" but understand the potential drawbacks.
#     * If you try something interesting that doesn't work, do report it!
#     * There is a potential to cheat (don't)
# * Re question on simplification: http://nlp.cs.swarthmore.edu/semeval/tasks/task10/summary.shtml
# * Next: MT Noisy Channel

# # Open Items Lecture 7 
# 
# * LectureCast: online! 
# * Next: [Word-based MT](/notebooks/chapters/word_mt_slides.ipynb), EM Algorithm
# 

# # Open Items Lecture 8
# 
# * Assignments: 
#     * It's possible to train most (unless neural) LMs by scanning through the data once and gathering counts
#     * You often do not need to calculate normalizers by explicit summing (e.g. in Absolute Discounting)
# * Next: CFGs in [Parsing](/notebooks/chapters/parsing_slides.ipynb)
# 

# # Open Items Lecture 9
# 
# * Assignment 2 coming out today!
#     * will be about feature engineering
#     * see lecture notes on text classification and sequence labelling
#     * you have 4 weeks! 
# * After reading week we will discuss possible MSc projects
# * Next: [Parsing](/notebooks/chapters/parsing_slides.ipynb), PCFG
# 

# # Open Items Lecture 10
# 
# * [South England NLP Meetups](https://www.meetup.com/South-England-Natural-Language-Processing-Meetup/?_cookie-check=BhaN3MFi2WYR6z7T)
# * Next: text classification
# 

# # Open Items Lecture 11
# 
# * Office hours: now at 90 High Holborn, still Friday 5 PM - 6 PM
# * Dec 6 & 8: Pontus will present
# * Last Week: Industry Speakers (currently: DeepMind, Benevolent AI)
# * Next: Conditional Log-Linear Models

# # Open Items Lecture 12
# 
# * Please fill out the attendance form
# * Assignment 1 Marking: 
#   * Students did change the "do not change" fields
#   * Students did submit code that didn't work (I will get in touch with you separately) 
# * Assignment 2 Typo: You get 20pts for Task 1!  
# * Office hours: now at 90 High Holborn, still Friday 5 PM - 6 PM
#     * But I will head over to the cafeteria in the Roberts building to chat for a while 
# * Next: Sequence Models

# # Open Items Lecture 13
# 
# * Assignment 2: Annotation Noise?
# * Next: Maximum Entropy Markov Models

# # Open Items Lecture 14
# 
# * Assignment 2: You can't save/submit your training parameters, but you can fix your hyperparameters
# * Thanks for amazing support on Moodle! 
# * Next: Unsupervised Relation Extraction

# # Open Items Lecture 15
# 
# * Assignment 1: Marks out today.
#   * Scores for description come with short justifications based on a global point system 
#   * Creativity vs Substance: KN standard on creativity, high on substance
# * Assignment 2: Due today! 
# * Assignment 3: coming out today 
# * Next: Dependency Parsing

# # Open Items Lecture 16
#  
# * Assignment 1 Marks
#   * Please be polite when requesting re-assessment or raise issues  
#   * We will not change the valuation of contributions, but if we find that other students got more points for "the same thing", we will adapt. 
#   * Please do make sure your code runs, and your text renders.
# * MSc Projects: 
#   * [Initial Ideas](https://docs.google.com/presentation/d/1fdgNcF49uAHUcyJyVp16mzMJ7pojPQ2zK7g0eRvjXwg/edit#slide=id.g2b7a5d13b8_4_2)
#   * [Previous projects](http://mr.cs.ucl.ac.uk/prev_msc_dissertations)
# * Next Week: Pontus Stenetorp
# * Next: More Deep Learning

# # Open Items Lecture 19
#  
# * Assignment 1 example solutions:
#   * modelling lower-case/upper case 
#   * caching: looking at the most recent histories and build a local LM + interpolate
#       * motivate by repetition in songs
#   * special treatment of `BAR`
#   * OOV parameters
#   * cross-validation of parameters to see robustness
#   * motivate higher order models by showing hit-rate
# * Next: injecting prior knowledge into NNs

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