Introduction to Machine Learning

Subscribe to Mailing List

NOTE: This course will be delivered in hybrid mode, i.e. the in-person lecture will be transmitted live on zoom. The face-to-face part of the hybrid courses will go ahead conditional on at least 5 participants registering for this mode of delivery. Few classes may be delivered exclusively online, so please check under the Future dates tab for clarification. The online part of the class will be supported by its own dedicated teaching staff (along with the lead lecturer), therefore participants can expect the same level of group and individual support as the face-to-face class. You will be able to choose your preferred mode of attendance (face-to-face or online) during registration.

Details

This course provides an overview of machine learning as a branch of artificial intelligence and some applications of machine learning. Supervised and unsupervised machine learning models and then explored for both numeric and binary outcomes. Finally, measures of model performance are discussed. The workshop session explores the use of a statistical software (R/Rstudio) for building machine learning models to real world datasets.

The following topics are covered:

· Definitions of Machine Learning and Artificial Intelligence

· Unsupervised Machine Learning (PCA and clustering)

· Supervised Machine Learning (discriminant analysis, nearest neighbour, decision tree, random forest, neural networks, etc)

· Assessing Performance of Supervised Learning Algorithms

· Cross-validation

· Steps in Model Building

· Extensions of Traditional Supervised Learning Algorithms

· Ethics in Machine Learning

The course is suitable for those who have a basic understanding of statistical concepts (such as summary statistics for numeric and categoric variables, confidence intervals and p-values) as these will not be taught during this course. Some knowledge of mathematical concepts like matrix addition, multiplication, inversion and transposing will be beneficial but not required.

Participants attending the R workshop need to have some knowledge of R to perform basic data manipulations and produce summary tables and graphs (these skills are taught on our one-day R course - Introduction to R).

Everyone wishing to attend the software workshop should ensure R and Rstudio are installed (installation guidelines will be made available two weeks before the course).

Fees

Course fees

External Delegates (Non-UCL)	£250.00
UCL Staff, Students, Alumni	£125.00 *
Staff and Doctoral Students from ICH/GOSH	FREE †

* Valid UCL email address and/or UCL alumni number required upon registration. Please note, this category does not include hospital staff unless you hold an official contract with the university.

† Limited free spaces available. If there are no free places remaining, Staff and Doctoral Students from ICH/GOSH can still register at the UCL rate.

Cancellations

Please note that no refunds will be given for non-attendance or cancellations made within 5 working days of the start of the course. For delegates attending courses on funded places, a £50 fee will be charged for late cancellation, non-attendance or partial-attendance. 

Future dates

Dates	Times	Apply
TBC	9.30am - 5.00pm^	Subscribe to general mailing list

^We recommend logging in 10 minutes prior to the scheduled start time to access materials and ensure the course can start promptly at 9.30am.