Introduction to machine learning and deep learning in Python, online
Monday, 04 November 2024 to Wednesday, 06 November 2024, 9.30am - 1pm
Apply for this courseThis course is a practical introduction to applying machine learning and deep learning algorithms on biological data using the Python programming language. Using a mixture of lectures and hands-on training, the participants will be taken through different steps of data processing, preparation and application of the machine learning techniques to solve various problems, with guidance from expert tutors. By the end of the course, the participants should be able to apply machine learning and deep learning techniques to tabular and image data.
You are required to attend 2 sessions:
- Monday 4 November, 9:30 - 13:00
- Wednesday 6 November, 09:30 - 13:00
COURSE FORMAT
Day 1
Theory: Introduction to machine learning, Types of problems that can be resolved with machine learning, Overview of machine learning algorithms
Hands-on: application of machine learning in Python using sklearn library to the publicly available medical dataset (solving classification problem)
Theory: Feature selection algorithms
Hands-on: application of feature selection and machine learning in Python using sklearn library to the publicly available biological dataset (solving regression problem)
Day 2
Theory: Introduction to image analysis with deep learning, Use cases, Image pre-processing
Hands-on: Pre-processing images in Python using Numpy and OpenCV libraries
Theory: Convolutional neural networks (CNNs) using Pytorch
Hands-on: Application of CNNs on an image classification problem (e.g. blood cell type classification)
Hands-on: Model evaluation, Using pre-trained models
COURSE OBJECTIVES
By the end of this course, you will learn:
How to pre-process your data for machine learning and deep learning analysis
How to apply machine learning techniques to address biological questions using example datasets
How to apply deep learning on an example image dataset
How to evaluate and visualise analysis results
what you will need
Basic knowledge and experience of coding in Python including following libraries: scipy, numpy, matplotlib, pandas
Each 3-hour session would consist of a mix of teaching and hands-on training, with a feedback from the tutors and Q&A at the end. You are expected to interact with the instructors and other course participants during the sessions for problem-solving and assignments. The code and worksheets for this course will become available at the first session and can be accessed anytime during and after the course.
Participants are required to have the following software installed on their computers before the course:
- Python (version 3.7 or higher) – download: https://www.python.org/downloads/
- Spyder (v5) - download: https://docs.spyder-ide.org/5/installation.html Note: alternative IDE such VS Code or Jupyter can be used
- A python virtual environment (e.g. Conda) is highly recommended
List of the Python SciPy libraries required for this tutorial: scipy, numpy, matplotlib, pandas, sklearn, Opencv, Pytorch,
Basic Python courses are available through the Oxford IT Learning Centre (for a registration fee):
Any installation questions could be addressed to courses@medsci.ox.ac.uk
Hardware requirements:
It is highly recommended to have access to a microphone and ideally a second screen during the sessions.
PARTICIPANT NUMBERS
25
ATTENDANCE CERTIFICATE ON SURVEY COMPLETION
In order to receive a certificate of attendance, participation in all three sessions is mandatory. Once you have completed the course, you will be sent an email with a link to the overall course feedback survey. When you have filled in and submitted the survey, you will be sent an email with a link to your attendance certificate. Survey results are downloaded and stored anonymously.