| CODE | CPS5129 | |||||||||
| TITLE | Research Topics in Data Science | |||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | |||||||||
| MQF LEVEL | Not Applicable | |||||||||
| ECTS CREDITS | 5 | |||||||||
| DEPARTMENT | Computer Science | |||||||||
| DESCRIPTION | This study-unit exposes students to recent advancements in techniques aimed at learning models from data, that typically represents text and images. Particular attention would be given to graphical models, deep models, and probabilistic grammar-based models. This reading study-unit will also focus on the data science aspects prior to model building (e.g. data collection, cleaning, model and feature selection methodologies, etc.) Students would be guided through a list of selected readings from relevant publications. Topics include but are not limited to: Integrated vision and language models, automated assessment models, spatio-temporal models, biological and computational chemistry models. At the beginning of the semester, a seminar will be held on: - The scientific method (how to identify a research problem, researching and determining relevant body of literature, implement a solution, evaluating the solution in a scientific manner); - How to read papers in an efficient and thorough manner; - How to write scientific documents (such as proposals, literature reviews, and dissertations), and - How to present scientific research. While the main aim of the seminar is to help students prepare the deliverables of this study unit, it will also provide solid background to tackle their final dissertation. To assist students with their reading, regular meetings are held with the lecturer to discuss the progress and any problems encountered. Once the student has read sufficiently, he or she is expected to draw up a report which reviews the texts under consideration. The review is expected to offer a mature discussion, comparing and contrasting the works within a sensible framework. Study-unit Aims: The aim of this study-unit is to expose the students to state of the art research papers in the area of Data science, help them organise their reading and research efforts while also giving them the opportunity to write a literature review. This will give the students invaluable background in the topics of data-driven modelling and analysis, while also giving them the opportunity of hands on research under close guidance from the lecturer. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Have an intimate appreciation of advanced state-of-the-art topics in Data Science; - Outline the research landscape of Data Science and explain how different areas of the topic relate to one another; - Choose the most suitable experimentation and results presentation methods in order to evaluate experimental ideas in the field; - Appraise a data science project and critically point out its shortcomings and assumptions; - Contrast different model-building techniques and be able to select one for the problem at hand. 2. Skills: By the end of the study-unit the student will be able to: - Discuss and critically analyze data science research papers covering current open problems in the field; - Search for literature containing background and related work to interesting papers and distill them into a literature review; - Present literature review in an oral presentation; - Plan a data science project, based on literature standards. Main Text/s and any supplementary readings: - The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009), Trevor Hastie, Robert Tibshirani, Jerome Friedman. Very popular advanced text. Sample readings: - A few useful things to know about machine learning. Pedro Domingos. Communications of the ACM CACM Homepage archive Volume 55 Issue 10, October 2012, Pages 78-87. - A survey on feature selection methods. Girish Chandrashekar, Ferat Sahin. Computers & Electrical Engineering. Volume 40, Issue 1, January 2014, Pages 16–28. - Representation Learning: A Review and New Perspectives. Y. Bengio, A. Courville, P. Vincent. IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume:35 , Issue: 8), 2013. - New Avenues in Opinion Mining and Sentiment Analysis. Erik Cambria, Bjorn Schuller, Yunqing Xia, Catherine Havasi. IEEE Intelligent Systems, March-April (2013 vol.28), pp: 15-21. - Machine-learning approaches in drug discovery: methods and applications. Antonio Lavecchia. Drug Discovery Today, Volume 20, Issue 3, March 2015, Pages 318–331. - Machine Learning in Virtual Screening. James L. Melville, Edmund K. Burke and Jonathan D. Hirst. Combinatorial Chemistry & High Throughput Screening, 2009, 12, 332-343. |
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| STUDY-UNIT TYPE | Lecture, Independent Study, Project and Seminar | |||||||||
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| LECTURER/S | Jean Paul Ebejer Reuben Farrugia Adrian F. Muscat |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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