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Department of
Computer Science
Tari Rorohiko

Computing and Mathematical Sciences

Upcoming Seminars

 

Non-parametric Methods for Unsupervised Semantic Modelling

Wray Buntine
Monash University, Melbourne, Australia
Monday 8 June 2015
11:00 am
G.1.15
This talk will cover some of our recent work in extended topic models to serve as tools in text mining and NLP (and hopefully, later, in IR) when some semantic analysis is required. In some sense our goals are akin to the use of Latent Semantic Analysis. The basic theoretical/algorithmic tool we have for this is non-parametric Bayesian methods for reasoning on hierarchies of probability vectors. The concepts will be introduced but not the statistical detail. Then I'll present some of our KDD 2014 paper (Experiments with Non-parametric Topic Models) that is currently the best performing topic model by a number of metrics.

 

Scalable text mining with sparse generative models

Antti Puurula
Department of Computer Science, The University of Waikato, Hamilton
Monday 8 June 2015
2:00 pm
G.1.15
About the talk: Antti will tell us about his PhD research, prior to his thesis oral.

The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks to utilize the information contained in vast document collections. General data mining methods based on machine learning face challenges with the scale of text data, posing a need for scalable text mining methods.

This thesis proposes a solution to scalable text mining: generative models combined with sparse computation. A unifying formalization for generative text models is defined, bringing together research traditions that have used formally equivalent models, but ignored parallel developments. This framework allows the use of methods developed in different processing tasks such as retrieval and classification, yielding effective solutions across different text mining tasks. Sparse computation using inverted indices is proposed for inference on probabilistic models. This reduces the computational complexity of the common text mining operations according to sparsity, yielding probabilistic models with the scalability of modern search engines.

The proposed combination provides sparse generative models: a solution for text mining that is general, effective, and scalable. Extensive experimentation on text classification and ranked retrieval datasets are conducted, showing that the proposed solution matches or outperforms the leading task-specific methods in effectiveness, with a order of magnitude decrease in classification times for Wikipedia article categorization with a million classes. The developed methods were further applied in two 2014 Kaggle data mining prize competitions with over a hundred competing teams, earning first and second places.

 

Improving Course Relevance: Techniques for Incorporating the Social Value of Computing into your Courses [Seminar/Workshop]

Mikey Goldweber
Department of Computer Science, Xavier University, Montgomery, Ohio
Tuesday 9 June 2015
11:00 am -12:30 pm
G.1.15
We begin by asking to what degree does a given undergraduate curriculum either reinforce impeding myths and misconceptions about computing or work to dismantle them. Particular focus should be placed on motivating examples and programming projects throughout the curriculum, with special focus on the introductory courses. Students' perceptions about computing, if they have a realistic opinion regarding computing at all, is strongly correlated with the programming projects they or their peers have worked on. Is computing seen as boring (duck counting), not serious (games), or maybe solely focused on business/commerce?

Research informs that students, particularly with respect to selection of major, seek to satisfy their values over their interests. It is time that computing curricula align accordingly. This workshop introduces "Computing for the Social Good: Educational Practices" (CSG-Ed). CSG Ed is an umbrella term meant to incorporate any educational activity, from small to large, that endeavors to convey and reinforce computing's social relevance and potential for positive societal impact.

The goal of this workshop is to equip computing educators with a set of techniques to create new CSG-Ed oriented assignments, or to repurpose old ones. From a first day activity in the first computing course, to the capstone experience, the opportunity exists to showcase in an integrated way the social value of computing across a broad spectrum of fields.

In the afternoon there will be an optional additional session, provided enough interest, for faculty to work with each other and/or the workshop facilitator on specific courses and/or assignments.

Bio: Mikey Goldweber has been a computing professor in the USA since 1990. He works primarily in computing education with foci in curriculum design, introductory programming, and pedagogic operating systems. For the past four years Mikey has been one of the driving forces behind the "Computing for the Social Good" movement in the computing education community. Finally, Mikey is the current Chair of the ACM Special Interest Group on Computers and Society (SIGCAS).

 

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