Computer Science

This course examines the main tasks and application scenarios in the field of computer vision, analyzes the technical difficulties in these tasks, and explains how to deal with these difficulties. It covers Point’s mainstream algorithms and analyzes and compare their respective effects and advantages and disadvantages.

This course provides an overview of data management architectures and analytics procedures aimed at organizing, describing, and modeling big data, both structured and unstructured. The course discusses both technical aspects of data management/analytics and topics related to analysis managerial evaluation including how to translate the outputs into meaningful business insights. The course examines topics including relational databases such as OLTP, Data warehouse, and SQL language; big data and NoSQL databases, distributed file system, Hadoop, Spark, and Data Lake concept; data understanding and data preparation; models and statistical techniques applied to Big Data; regression and classification trees; ensemble methods (random forest and boosted trees); logistic regression; supervised artificial neural networks; models' performance evaluation; big data ingestion and management; data preparation and cleaning; machine learning algorithms application; and machine learning model evaluation. The course requires students have a basic understanding of descriptive and inferential statistics and basic computer skills as a prerequisite.

This course reviews issues associated with the strategic sourcing of information systems. The main focus is to understand and evaluate different sourcing strategies for information systems. The course describes various sourcing solutions for development and maintenance as well as the management of information systems. The aim is to equip students with the necessary knowledge in order to be able to assess and evaluate different sourcing strategies for information systems. Some of the issues discussed are: What solutions are there for a company that wishes to implement information systems? What advantages and disadvantages do the different solutions involve? What makes one solution fit better than the other?

In this course, students learn the skills to write Python code to implement statistical and machine learning algorithms that can be applied in a range of contexts. Each week the course covers an aspect of computer coding using examples and exercises that drawn on bioscience contexts. Topics will include: probability, maximum likelihood, Bayes theorem, supervised learning: regression and classification, unsupervised learning: dimensionality reduction and clustering, model evaluation and improvement, reinforcement learning, and neural networks and deep learning.