The aim of this module is to enable students to integrate systematic and structured history taking with clinical assessment, physical examination and...

Clinical Assessment and Diagnostic Skills (PHAY0062)

Computer Science|Data Collection and Processing|Physics

Outline:

The course aims to provide an introduction to the use of machine learning in the context of physics data handling and analysis. Topics: Fundamental...

Practical Machine Learning for Physicists (PHAS0056)

Outline:

The course aims to provide an introduction to the use of machine learning in the context of physics data handling and analysis. Topics: Fundamental concepts in machine learning and modern statistics such as bias-variance, overfitting, regularization and generalization. Advanced topics in supervised and unsupervised learning: ensemble models, deep learning, neural networks, clustering and data visualization.

Aims:

After taking this course, students should:

understand the key concepts of machine learning;
	know how to create clear, reliable and maintainable code;
	be able to utilize machine learning and artificial intelligence techniques to analyse datasets such as those that arise in level-6 laboratories and level-6 and level-7 projects.
Teaching and Learning Methodology:

The course is taught via lecture and interactive practical sessions. All of the session time in the course will be devoted to hands-on practice on the computers.

In addition to timetabled lecture hours, it is expected that students engage in self-study in order to master the material. This can take the form, for example, of practicing example questions and further reading in textbooks and online.

This module is a laboratory-based research project. You will conduct a short programme of original research in the laboratory of a member of academic staff in...

Advanced Research Project in Pharmacology (PHAR0017)

This module is a laboratory-based research project. You will conduct a short programme of original research in the laboratory of a member of academic staff in the Department of Neuroscience, Physiology and Pharmacology (NPP) or in one of the laboratories of our associated Institutes. You will write up your project a scientific report to include a substantial account of related published work and of the methods used as well as a description and discussion of the results obtained.

All BSc students wishing to do a laboratory project must have successfully progressed with an overall mark of at least 60% iBSc - students must have successfully completed year 1 and 2 of the MBBS programme.

You can apply for projects through the NPP portal, selecting 5 potential supervisors from a list After considering your academic details, if a supervisor is interested in your application, they will contact you by e-mail with a view to arranging a meeting/interview. Alternatively you may apply for projects with supervisors based at UCL but not listed on the portal but must consult with the project tutor before undertaking such a project.

After taking this module you will have:

Carried out a research project on an offered subject/topic, or an alternative agreed with the supervisor,
	Gained experience in laboratory research, including the design and implementation of experiments, related to the pharmacological sciences.
	Learned to analyse experimental data. (
	Used relevant literature to inform the design, analysis and interpretation of the experimental data obtained.
	Written a clear and knowledgeable dissertation, including an account of the background of the area of research, the methodology used, the results obtained and discussion of the conclusions that can be drawn from the results in the context of the current knowledge available from the literature.