School of Engineering and Computer Science Te Kura Mātai Pūkaha, Pūrorohiko

Lectures

This page is under construction. The schedule may change and the slides will be updated during the course.

num	Topic	Resources

1	Introduction, Course Organisation	slides pdf
2	Classification, KNN, Text Features?	slides pdf, diabetes.csv, classifier-KNN.ipynb
3	TF.IDF, Simple text classifier	slides pdf, lecture-tfidf.ipynb, 4docs.csv
4	Presentations	lecture-simple-text-classification.ipynb, simple-review.csv, lecture-nltk.ipynb
5	word embedding, word2Vec, CBOW, Skipgram	slides pdf, visualisation at https://ronxin.github.io/wevi/
6	CNN for text classification	pdf, Visualization at https://stats.stackexchange.com/questions/296679/what-does-kernel-size-mean
7	Python implementation, train embedding, pre-trained	slides pdf, Sample code on Google Colab
8	More recent research in text representation, BERT	slides pdf, Use BERT in Python
9	Projects, my research	slides pdf
10	Attention, Transformer	week6-Attention.pdf, BertViz at https://colab.research.google.com/drive/1hXIQ77A4TYS4y3UthWF-Ci7V7vVUoxmQ?usp=sharing#scrollTo=twSVFOM9SopW
11	Recommender Systems	week7-RC.pdf
12	Content based Recommender systems	pdf
13	Information Retrieval, Google and PageRank	week9-IR.pdf
14	Personalised Search, Evaluation	pdf
15	Query Expansion	week10-AQE.pdf
16	Language modeling, Attention, Transformer
17	Machine Translation
18	Review, NLP and applications

Presentation sign up

The topics are not limited to the ones listed below. You are encouraged to choose new topics or recent research areas, for example, Sora, Gemini, ChatGPT/chatbots, large language models, text generation, automatic essay scoring, speech recognition, text understanding, caption generation, tools such as spaCy (used in industry), StanfordNLP (research), beautiful soup, etc. If you choose a topic that is not covered in lectures, you will need to give an introduction, history, state-of-art technology, leading researcher or typical system, future research direction etc; If you choose a topic listed below which will be covered in lectures, you will need to find more recent research e.g a recent paper or a new system/technology.

week 2	Thursday, 7 March, topics related to text classification or clustering, such as new algorithms, deep learning models, or their applications
sign up	Adam Glucksman, Sam Mata, Thomas Yang, Jessie Dong, Magnus Bugge, Nicholas McCormick
week 4	Thursday, 21 March, topics related to text representation such as word2vec, word embedding, word rank, new measures for word similarity
Sign up	Nanda Aung, Ella Wipatene, Hadas Cohen, (Joy) Huixin Guan, Ye Li, Guangyong Yang
week 6	Thursday, 18 April, topics such as clustering algorithms, opinion mining, information extraction
Sign up	Millie Day, Serafina Slevin, Braeden Windle, Rhys Hanrahan, Pierce Burt, Annie Cho
week 8	Thursday, 2 May, topics related to recommender systems, such as the system used by TikTok, Netflix, Amazon, youTube, etc.
sign up	Chuan Law, Jack Grunfeld, RuoHao Sun
week10	Thursday, 16 May, topics related to information retrieval, query expansion, personalised search, such as new search engines, new web services.
Sign up	Yuxuan Zhang, Damiana Fitria K, Renswick Delvar, William Shondelmyer, Thomas Legge, Vijai Simmon
week12	Thursday, 30 May , other topics such as machine translation, other natural language processing tasks
Sign up