Based on a previous version by Michael Gutmann.
|wk1||Thu 18/01||lecture 1|
|wk2||Wed 24/01||lab 1||Thu 25/01||lecture 2|
|wk3||Wed 31/01||lab 2||Thu 01/02||lecture 3|
|wk4||Wed 07/02||lab 3||Thu 08/02||lecture 4|
|wk5||Wed 14/02||lab 4||Thu 15/02||lecture 5|
|wk6||Thu 01/03||poster session|
|wk7||Thu 08/03||poster session|
|wk8||Thu 15/03||poster session|
|wk9||Thu 22/03||poster session|
|wk10||Thu 29/03||poster session|
|wk11||Thu 05/04||Recap, Q&A|
|Deadline for your paper preference||Fri 9 Feb 2018, 4pm|
|Deadline for your project info||Fri 16 Feb 2018, 4pm|
|Mini-project interim report deadline||Tue 13 March 2018, 4pm|
|Mini-project final report deadline||Fri 6 April 2018, 4pm|
Wednesdays: 09:00 - 10:50
Appleton Tower, room 6.06
Lectures, poster sessions:
Thursdays: 15:10 - 17:00
1 George Square (Neuroscience), G.8 Gaddum Lecture Theatre
The lecture is accompanied by lecture notes (they will be updated as we progress).
- Lecture 1
Introduction to the data analysis process, simple descriptions and preprocessing of data
- Lecture 2
Principal component analysis
- Lecture 3
Probabilistic PCA, dimensionality reduction by PCA
- Lecture 4
Dimensionality reduction by kernel PCA, multidimensional scaling, isomap
- Lecture 5
Evaluating the performance in predictive modelling (e.g. classification and regression), techniques for choosing hyper-parameters
The course has eight computer labs (two per week) on topics introduced in the lecture. The labs will allow you to play with different methods to gain some intuitive understanding and provide you with practical tools for the mini-project. There is a GitHub repository for the labs.
- Lab 1 on simple data descriptions and preprocessing
- Lab 2 on principal component analysis
- Lab 3 on dimensionality reduction
- Lab 4 on performance evaluation and hyperparameter/model selection
In the second half of the course, we will have poster presentations on some of the papers listed on the papers page. Feel free to propose papers yourself but please check with the lecturer about suitability.
Detailed instructions and information on the format of the presentations can be found on the papers page.
The goal of the project is to apply machine learning methods to a real dataset. We have a list of potential datasets (same as for the IRDS course). For each dataset, the web page gives a description of the task to be undertaken. You will produce a project report that will be assessed.
Please have a look at the mini-projects page for detailed instructions and information on the format of the report.
The breakdown of your total course grade is as follows: 50%: exam; 35%: mini-project; 10%: poster presentation; 5%: the presentation summaries.