Schedule and course plan

Period 3

Where and When	Activity	Reading	Examination
January 19 13.15-15.00 V2	Lecture 1: Introduction, boolean retrieval, course practicalities Hedvig Kjellström	Manning Chapter 1
January 22 10.15-12.00 D3	Lecture 2: Term vocabulary, dictionaries and tolerant retrieval Johan Boye	Manning Chapter 2, 3
January 26 13.15-15.00 B3	Lecture 3: Evaluation of search engines Jussi Karlgren	Manning Chapter 8
February 9 13.00-17.00 Röd 17.00-18.00 Orange	Computer hall session		Oral examination of Assignment 1 in front of computer
February 12 10.15-12.00 D3	Lecture 4: IR Beyond one shot Jussi Karlgren	Manning Chapter 9, Robertson Read after Lecture 5
February 16 13.15-15.00 B1	Lecture 5: Scoring, weighting, vector space model Hedvig Kjellström	Manning Chapter 6, 7 Read before Lecture 4
February 23 13.15-15.00 B3	Lecture 6: Retrieval of documents with hyperlinks Hedvig Kjellström	Manning Chapter 21, Avrachenkov Sections 1-2
February 24 10.15-12.00 B3	Lecture 7: Probabilistic information retrieval, language models Hedvig Kjellström	Manning Chapter 11, 12
March 8 13.00-18.00 Orange	Computer hall session		Oral examination of Assignment 2 in front of computer
March 11 10.15-12.00 B1	Lecture 8: Some useful additions to a search engine, Random Indexing Viggo Kann	Sahlgren Manning Chapter 18

Period 4

Where and When	Activity	Reading	Examination
April 5 13.00-18.00 Orange	Computer hall session		Oral examination of Assignment 3 in front of computer
April 12 13.15-14.00 B1	Lecture 9: Guest lecture Hercules Dalianis, SU Clinical text retrieval - some methods and some applications Electronic patient records contain a waste source of information, both in form of structured information as diagnosis codes, drug codes, lab values, time stamps, etc and unstructured in form of free text. Methods - both rule based and machine learning based for retrieving this information is presented. Applications as diagnosis codes assignment, hospital acquired infection detection and adverse drug event detection will be discussed.
April 26 13.15-15.00 M3	Lecture 10: Guest lectures Maria Skeppstedt, Linnaeus University and Gavagai Sentiment, stance and applications of distributional semantics Sentiment analysis aims at determining the attitude of the speaker/writer, (e.g., 'positive' or 'negative'), while stance detection aims at determining the speaker's/writer's position on an issue (e.g., 'for' or 'against' an issue). What methods could you use for sentiment analysis/stance detection and what are the practical applications? How could you use distributional semantics? Examples from products from the Swedish language technology company Gavagai will be given, as well as from the StaViCTA project at Linnaeus University. Roelof Pieters, KTH and Graph Technologies Multi-modal Retrieval and Generation with Deep Distributed Models Multi-modal retrieval is emerging as a new search paradigm that enables information retrieval from various types of media. For example, users can simply make a photo of a movie poster to search for relevant reviews and trailers. Key to such approaches are very deep neural networks which can be trained to represent such diverse data extremely well. Once "embedded" in such a space, one can even turn such neural networks upside down to create new novel data. Imagine a neural network that can compose songs, or write novels in the style of Shakespeare! The talk will give a short overview of Deep Learning approaches, multi-modal embedding methods, to then show some examples of recent and ongoing work in multimodal search and generation.
April 29 9.00-12.00 Brun	Extra computer hall session
May 3 13.15-15.00 Q2	Lecture 11: Guest lectures Boxun Zhang, Spotify Introduction to Locality Sensitive Hashing In this lecture, I will first give a brief introduction of locality sensitive hashing, a widely-used algorithm for nearest neighbor search, hierarchical clustering, audio fingerprint, and etc. Then, I will describe how we use this algorithm at Spotify. Anders Friberg, KTH Music Information Retrieval The recent paradigm shift in music distribution and production has created a need for developing methods of music audio content analysis. This is one of the major directions within the field music information retrieval and its aim is to understand music audio similar to how human listeners perceive music. An overview of methods and challenges in the field with some snapshots from KTH research will be presented.
May 10 13.15-15.00 B3	Lecture 12: Guest lectures Simon Stenström, Findwise Search From the Trenches What distinguished a search engine from a search solution? This talk focuses on the part of findability that a search engine just doesn't do well on its own. How can we solve that? What else do you need? Magnus Rosell, FOI Classified
May 20 09.00-12.00 Fantum, Lindstedtsv 24, floor 5	Project presentations		Written report hand-in Oral presentation in front of poster