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Machine Learning Practical (MLP) 2018-19
- Latex template for final report: mlp-cw4-template.zip
- Latex template for interim report: mlp-cw3-template.zip
- Project discovery guide, and ideas for possible projects
- Coursework 4 description available
- Coursework 3 description available
- Results and feedback from mid-semester survey. Thanks to everyone who participated.
- Coursework 2 is now available
- ML-Base is now 5-6pm, Monday-Friday in AT-7.03
- ML-Base: Monday-Friday 5-6pm in AT-7.03 (the InfBase room), starting Monday 8 October. ML-Base will have a tutor to answer questions to do with the machine learning courses (MLP / MLPR / IAML); it is also a time and place to drop in to work and discuss problems, and meet people taking machine learning classes. (If you have questions about the specific software frameworks used in the courses, these are best asked in the lab sessions for the course.)
- Office hour now in Informatics Forum Atrium (ground floor) in the cafe-style area - same time, 4pm on Tuesdays, after the lecture
The Machine Learning Practical (MLP) for 2017-18 will be is concerned with deep neural networks. Doing this course involves the following:
- Implementing deep learning systems using python;
- Training and evaluating on data sets for tasks such as handwriting recognition;
- Designing and running machine learning experiments to investigate research questions;
- Reporting on your experiments, discussing and interpreting the results.
During semester 1 we shall investigate neural network learning with a focus on the classification of handwritten digits using the well-known MNIST dataset. Using a Python software framework that we shall provide, and a series of Jupyter notebooks, the course will explore multi-layer neural network classifiers, convolutional network classifiers, and recurrent networks. The lectures in semester 1 will provide the required theoretical support for the practical work.
Semester 2 will be based around group projects, typically using TensorFlow, PyTorch, or another deep learning toolkit. The lectures in semester 2 will cover more advanced material in deep learning.
The MLP course requires mathematical ability (calculus, linear algebra, and probability) and programming ability (the course will be based on Python using Numpy. Some previous experience of machine learning is is extremely helpful.
Undergraduates: If you have taken Informatics 2B and IAML, and can program, you are qualified to do the course.
It is also recommended to take MLPR.
- Teaching assistant:
- Locations -- Semester 1
- Lectures on Tuesday 15:10, Gordon Aikman Lecture Theatre, George Square
- Labs: A number of weekly lab sessions will be scheduled in semester 1, students are expected to attend one of these.
- ML-Base: Monday-Friday 5-6pm in AT-7.03 (the InfBase room), starting Monday 8 October.
ML-Base will have a tutor to answer questions to do with the machine learning courses (MLP / MLPR / IAML); it is also a time and place to drop in to work and discuss problems, and meet people taking machine learning classes. (If you have questions about the specific software frameworks used in the courses, these are best asked in the lab sessions for the course.)
- Office hours: Tuesdays, 16:10-17:00, Informatics Forum Atrium (ground floor) in the cafe-style area.
If you have a question about the course, then you are welcome to come along and ask/discuss. If your question is about the software, the environment, or implementation issues relating to the labs or coursework, then please ask your questions in the lab, rather than the office hour.
- Locations -- Semester 2
- Official course pages
- MLP course Q&A on Piazza
- Information on auditing the class, or taking it not for credit. If you are auditing the class and the room appears becomes full then please leave to allow fully-registered students to attend.
- Course web pages for last year (2017-18) - note that the MLP github will be updated and reset for the start of this years course..
Copyright (c) University of Edinburgh 2015-2018
The MLP course material is licensed under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
The software at https://github.com/CSTR-Edinburgh/mlpractical is licensed under the Modified BSD License.
This page maintained by Steve Renals.
Last updated: 2019/09/16 15:42:09UTC
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