All-provider course timetable

Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book by linking here.

This course is part of the Scientific Computing series.

This course is aimed at those new to programming and provides an introduction to programming using Python, focussing on scientific programming. This course is probably unsuitable for those with programming experience, even if it is just in shell scripting or Matlab-like programs. By the end of this course, attendees should be able to write simple Python programs and to understand more complex Python programs written by others.

As this course is part of the Scientific Computing series, the examples chosen are of most relevance to scientific programming.

This interactive and intensive one-day course, based on the programme developed by Vitae, is intended for PhD students in their first year. You will look at practical ways to increase your effectiveness and meet the challenges of your PhD. The programme covers:

• Looking at your PhD as a project
• Working with other people
• Managing your relationship with your supervisor

Anyone who is a first-time user of unsealed radioactive substances is expected to attend this course before they begin the work. Users of radioactive substances who are new to the University should also attend. This course also acts as a refresher for experienced users and for users from other institutions or countries. There will be time for discussion and questions at the end of the session, and a self-assessment exercise of attendees’ core knowledge will be carried out.

This course complements the supervising training and information your Department will provide. It is a course that consists of: an online module, which introduces practices and principles of undergraduate supervision at Cambridge, and a face-to-face workshop in which you will explore challenges and approaches to supervising.

By the end of this course you will know:

• the purpose of supervisions at Cambridge
• how to deal with common supervision scenarios
• how to provide effective feedback
• the practicalities of starting to supervise

Topics covered:

• Introduction to supervising (including what supervisions are, format, preparing for them)
• Background information for those who need it (British education system, Cambridge undergraduate system)
• Role of a supervisor
• Dealing with different supervision scenarios
• Departmental information
• Summary of what you have learnt
• Practical tips and advice
• Resources for ongoing support and information

This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course.

One session covering the Medline database, targeted at doctors. Learn how to get the best out of your literature searches.

From 6 April 2017 new rules will apply to payments made to limited companies largely controlled by a single individual where they provide services to the University, or agencies providing such resource to the University. The Finance Division is holding a number of seminars to explain procedures and guidelines that they are implementing to help departments manage this risk.

This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:

• The key features of quantitative analysis, and how it differs from other types of empirical analysis
• Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
• Features of statistical distributions: measures of central tendency and dispersion
• The normal distribution
• The basics of formal hypothesis testing
• Why statistical testing works
• Statistical methods used to test simple hypotheses
• How to use Stata

A workshop to enable you to practice and learn relaxation techniques to help release stress and improve wellbeing. It will provide an opportunity for people to relax and take time for themselves. The workshop will include deep breathing techniques, progressive muscle relaxation and a short mindfulness exercise. It will also allow time to consider what stress is and the impact this has on our lives.

This is an introduction to programming in modern C++, based on the book "'Programming: Principles and Practice using C++"' (2nd ed.) by Bjarne Stroustrup. The aim is to teach participants how to write non trivial, practical programs that are comprehensible and portable. Participants should also be able to understand and modify most well-written C++ applications, though not necessarily every aspect of them.

C++ is a large and complicated language, which is reflected in the length of this course. The creator of C++, Prof. Stroustrup, estimates that newcomers to programming will have to devote in excess of 200 hours' of work to learn how to program in C++ properly. Please bear that in mind if signing up for the course. It would also be of help (though not essential) if attendees have some prior programming experience in another language, e.g. Python.

The course is designed to take someone from having no knowledge of the Unix command line to being able to navigate around directories, and doing simple file manipulation. Then some of the more basic commands, will be introduced, including information on how to get more help from the system itself. Finally accessing remote computers by ssh and the most basic of shell scripts will be introduced.

This course will constitute a practical introduction to experimental method and design suitable for students from any discipline who have had limited experience of empirical methods but who wish to be able to read and understand the experimental literature or to undertake their own experimental studies. The course includes:

• A theoretical introduction to the concepts and practices involved in experimental research in the human sciences, including ethical considerations;
• An introduction to experimental design and to appropriate analytic techniques;
• A practical component that can be undertaken away from the laboratory; and
• An introduction to issues involved in writing up results.

At the end of the module, students will be equipped with the fundamental knowledge required to design and evaluate an experiment.

CamSIS Search is a tool used within CamSIS to create lists of students and update student data. This course will show you how to understand the data structure and how to use CamSIS Search to create student lists.

This course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

1. Use R confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

R is a free, software environment for statistical and data analysis, with many useful features that promote and facilitate reproducible research.

In this course, we introduce the R language, and cover basic data manipulation and plotting. We then move on to explore classical statistical analysis techniques starting with simple hypothesis testing and building up to generalised linear model analysis. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.