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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="description" content="Website for the Machine Learning for Neuroscience module.">
<meta name="author" content="Alex Capstick, Nan Fletcher-Lloyd and Payam Barnaghi">
<meta property="og:title" content="ML4NS Imperial" />
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<title>ML4NS Imperial</title>
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<link href="css/bootstrap.css" rel="stylesheet">
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<link href="css/one-page-wonder.css" rel="stylesheet">
</head>
<body>
<nav class="navbar navbar-fixed-top navbar-inverse" role="navigation">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-ex1-collapse">
<span class="sr-only">Toggle navigation</span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
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</button>
<a class="navbar-brand" href="#">ML4NS</a>
<div class="theme-button-phone">
<label class="theme-switch">
<input type="checkbox" id="checkbox1" />
<div class="slider round"></div>
</label>
</div>
</div>
<!-- Collect the nav links, forms, and other content for toggling -->
<div class="collapse navbar-collapse navbar-ex1-collapse">
<ul class="nav navbar-nav">
<!--<li><a href="#teaching_staff">Teaching Staff</a></li>-->
<li><a href="#content">Content</a></li>
<li><a href="#assessment">Assessments</a></li>
<li><a href="#schedule">Schedule</a></li>
<li><a href="#lectures">Lectures</a></li>
<li><a href="#labs">Labs</a></li>
</ul>
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</nav>
<div class="header-image">
<div class="headline" id="top_page">
<div class="container">
<h1>Machine Learning for Neuroscience</h1>
<h2>
Translational Machine Intelligence Lab
</h2>
<div class="brand-logo-container">
<image class="brand-logo-image" src="./assets/logo.svg"></image>
</div>
</div>
</div>
</div><!-- /header-image -->
<div class="container">
<!-- START THE FEATURETTES -->
<hr class="featurette-divider">
<div class="featurette" id="teaching_staff">
<h3 class="featurette-heading">Teaching Staff</h3>
<h4>Members of the <a href="https://tmi-lab.github.io/" target="_blank">Translational Machine
Intelligence Lab</a> delivering this module:</h4>
<div class="staff-image-container">
<ul class="staff-image-list">
<li>
<div><a href="https://www.linkedin.com/in/nan-fletcher-lloyd/" target="_blank"><img class="staff-image"
src="./assets/nan_fletcher_lloyd.jpeg"></a>
<p class="staff-names">Nan Fletcher-Lloyd</p>
</div>
</li>
<li>
<div><a href="https://alexcapstick.github.io/" target="_blank"><img class="staff-image"
src="./assets/alex_capstick.jpeg"></a>
<p class="staff-names">Alex Capstick</p>
</div>
</li>
<li>
<div><img class="staff-image" src="./assets/yu_chen.jpeg">
<p class="staff-names">Yu Chen</p>
</div>
</li>
<li>
<div><img class="staff-image" src="./assets/anastasia_gailly_de_taurines.jpeg">
<p class="staff-names">Anastasia Gailly De Taurines</p>
</div>
</li>
<li>
<div><img class="staff-image" src="./assets/antigone_fogel.jpeg">
<p class="staff-names">Antigone Fogel</p>
</div>
</li>
<li>
<div><img class="staff-image" src="./assets/iona_biggart.jpeg">
<p class="staff-names">Iona Biggart</p>
</div>
</li>
<li>
<div><a href="https://www.imperial.ac.uk/people/p.barnaghi" target="_blank"><img class="staff-image"
src="./assets/payam_barnaghi.jpeg"></a>
<p class="staff-names">Payam Barnaghi</p>
</div>
</li>
</ul>
</div>
<h3 class="featurette-heading">Alumni</h3>
<h4>Past members who helped make this module possible:</h4>
<p>We would like to give thanks to Marirena Bafaloukou, Anastasia Ilina, Tianyu Cui, Ruxandra Mihai, Olivia Li, and Francesca Palermo.</p>
</div>
<hr class="featurette-divider" id="content">
<div class="featurette">
<h3 class="featurette-heading">Content</h3>
<div>
<p>Deepen your understanding of machine learning as applied to neuroscience. This course will cover
supervised and unsupervised techniques as well as classic machine learning methods and deep learning.</p>
<h4>What will you do in this course?</h4>
<p>This course will explore machine learning methods with a discussion on their use in neuroscience. We will
cover several aspects of machine learning, from classical methods to deep learning. </p>
<p>
By the end of this module, you will be able to:
<ul>
<li>
<strong>ILO 1:</strong> Critique and contrast machine learning techniques and analyse the problem settings
within which they are particularly useful.
</li>
<li>
<strong>ILO 2:</strong> Develop machine learning models by applying common machine learning libraries and
tools.
</li>
<li>
<strong>ILO 3:</strong> Apply practical computational and machine learning techniques to analyse data
from a variety of sources and interpret the results.
</li>
<li>
<strong>ILO 4:</strong> Plan and implement neural networks to develop end-to-end solutions for data analysis
problems.
</li>
<li>
<strong>ILO 5:</strong> Justify the choice of applied machine learning models based on their appropriateness for
the problem, ability to generalise and limitations.
</li>
</ul>
</p>
<h4>Essential Course Material</h4>
<p>Before taking this module, we advise that you work through the following video tutorials:</p>
(Videos and slides for the Python tutorial are created by Nan Fletcher-Lloyd, Iona Biggart, and Antigone Fogel)
<hr class="featurette-divider" id="python-tutorial">
<ul>
<li>
Video tutorial 1: Installing Anaconda
<a target="_blank"
href="https://youtu.be/s-t0jJg0iiU">(video)
</a>
<a target="_blank"
href="python-slides/1. Installing Anaconda.pptx">(slides)
</a>
</li>
<li>
Video tutorial 2: VScode and Jupyter Notebooks
<a target="_blank"
href="https://youtu.be/w5F9mLvTDsE">(video)
</a>
<a target="_blank"
href="python-slides/2. VSCode and Jupyter Notebooks.pptx">(slides)
</a>
</li>
<li>
Video tutorial 3: Python and Machine Learning in Neuroscience
<a target="_blank"
href="https://youtu.be/YMuHuOypiEA">(video)
</a>
<a target="_blank"
href="python-slides/3. Python and Machine Learning in Neuroscience.pptx">(slides)
</a>
</li>
<li>
Video tutorial 4: Data Types and Structures in Python
<a target="_blank"
href="https://youtu.be/ueGZNu0xFUo">(video)
</a>
<a target="_blank"
href="python-slides/4. Data Types and Structures in Python.pptx">(slides)
</a>
</li>
<li>
Video tutorial 5: Lists & Dictionaries
<a target="_blank"
href="https://youtu.be/qQ1hKbFJCPI">(video)
</a>
<a target="_blank"
href="python-slides/5. Lists & Dictionaries.pptx">(slides)
</a>
</li>
<li>
Video tutorial 6: NumPy Arrays
<a target="_blank"
href="https://youtu.be/_8e5XvAynSU">(video)
</a>
<a target="_blank"
href="python-slides/6. NumPy Arrays.pptx">(slides)
</a>
</li>
<li>
Video tutorial 7: Pandas Dataframes
<a target="_blank"
href="https://youtu.be/o94Rg4XJ43M">(video)
</a>
<a target="_blank"
href="python-slides/7. Pandas DataFrames.pptx">(slides)
</a>
</li>
<li>
Video tutorial 8: Plotting Pt. 1
<a target="_blank"
href="https://youtu.be/aeV6U8de1RY">(video)
</a>
<a target="_blank"
href="python-slides/8. Plotting Pt. 1.pptx">(slides)
</a>
</li>
<li>
Video tutorial 9: Plotting Pt. 2
<a target="_blank"
href="https://youtu.be/N_OLX27lSk0">(video)
</a>
<a target="_blank"
href="python-slides/9. Plotting Pt. 2.pptx">(slides)
</a>
</li>
<li>
Video tutorial 10: Conditional Statements
<a target="_blank"
href="https://youtu.be/70V5eNzg8LM">(video)
</a>
<a target="_blank"
href="python-slides/10. Conditional Statements.pptx">(slides)
</a>
</li>
<li>
Video tutorial 11: For Loops
<a target="_blank"
href="https://youtu.be/c1XcXSK0grI">(video)
</a>
<a target="_blank"
href="python-slides/11. For Loops.pptx">(slides)
</a>
</li>
<li>
Video tutorial 12: User-defined Functions
<a target="_blank"
href="https://youtu.be/658yioDLyDA">(video)
</a>
<a target="_blank"
href="python-slides/12. User-defined Functions.pptx">(slides)
</a>
</li>
<li>
Our Journeys
<a target="_blank"
href="https://youtu.be/gu-oD_6AYVs">(video)
</a>
</li>
</ul>
<p> If you have any trouble installing anaconda or VSCode, please do not hesitate to contact us. </p>
<p> We also suggest that you complete the <a target="_blank" href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/01-%20Python%20Tutorial%20Preliminary%20Skills%20and%20Concepts/01%20-%20Python%20for%20Beginners.ipynb">
Python for Beginners Tutorial</a>. If you have any difficulty/questions arising from this, we would be happy to work through these
with you during the first lab.</p>
<h4>Reading List</h4>
<p>A good place to start is <a target="_blank" href="https://probml.github.io/pml-book/book1.html">Kevin
Murphy's book on Probabilistic Machine Learning</a>. You might find it helpful to read the following sections:
</p>
<ul>
<li>The Introduction</li>
<li>Foundations: Probability: Univariate Models</li>
<li>Foundations: Linear Algebra</li>
</ul>
<p>
If you get through this list and would like some additional reading, let us know and we can recommend more!
</p>
<h4>Optional Course Material</h4>
<p>In addition to the essential course material, a knowledge of linear algebra, probability theory and calculus
will give you the best chance of success.</p>
<ul>
<li>
Linear Algebra Tutorial (Adapted from Kevin Murphy's <a target="_blank"
href="https://github.com/probml/pyprobml/blob/master/notebooks/book1/07/linalg.ipynb">linalg.ipynb
</a> Tutorial):
<a target="_blank" class="notebook-link"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/Linear%20Algebra%20Tutorial.ipynb">
Linear Algebra Tutorial.ipynb
</a>
</li>
<li>
Normalisation, Scaling, and Imputation:
<a target="_blank" class="notebook-link"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/Normalisation%20Scaling%20Imputation.ipynb">
Normalisation Scaling Imputation.ipynb
</a>
</li>
<li>
Pytorch Tutorial:
<a target="_blank" class="notebook-link"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/Pytorch%20Tutorial.ipynb">
Pytorch Tutorial.ipynb
</a>
</li>
</ul>
</div>
</div>
<hr class="featurette-divider" id="assessment">
<div class="featurette">
<h3 class="featurette-heading">Assessments</h3>
<div>
<h4>What are you being assessed on?</h4>
<p>We will assess your ability to apply the knowledge you haved learnt during the lectures.
Questions will test your understanding of key machine learning concepts as well as your ability to apply machine learning
methods in Python to a series of real-world problems.</p>
<h4>How will this be assessed?</h4>
<p>Through this course, you will be assessed on four piecess of coursework: three pieces of coursework completed during lab three pieces of coursework during lab sessions, and a final project. You will
also be given the opportunity to complete a formative assessment (marks do not count towards your final grade for this module) for feedback on your understanding of key techniques.
Marked assessments will be made available to you on Blackboard at the start of the afternoon lab sessions.
</p>
</div>
</div>
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<hr class="featurette-divider" id="schedule">
<div class="featurette">
<h3 class="featurette-heading">Schedule</h3>
<p>All lectures are recorded, but the labs will not be.</p>
<div class="schedule-container">
<table class="schedule-table">
<caption>Week 1 - Starting Monday 13th January</caption>
<tr>
<th style='width:8%'>Date</th>
<th style='width:12%'>Time</th>
<th style='width:27%'>Content</th>
<th style='width:12%'>Files</th>
<th style='width:11%'>Activity</th>
<th style='width:10%'>Feedback</th>
<th style='width:20%'>Lecturer</th>
</tr>
<tr>
<td rowspan="2">Mon</td>
<td>10:30 - 12:30</td>
<td>1: Introduction to Machine Learning</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/01-%20Introduction/Lecture1_Introduction.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
|
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/notes/01-Introduction%20to%20Machine%20Learning.pdf">
<img class="files-icon" src="/assets/notes.svg"></img>
</a>
</td>
<td> Lecture</td>
<td>
<a target="_blank" href="https://forms.office.com/e/qXMvfu1TqN" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td>Key Machine Learning Techniques ☆</td>
<td>
<a target="_blank" class="files-icon-container notebook-link-table"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/01-%20Python%20Tutorial%20Preliminary%20Skills%20and%20Concepts/01%20-%20Machine%20Learning%20for%20Beginners.ipynb">
<img class="files-icon" src="/assets/notebook.svg"></img>
</a>
<br>
<a target="_blank" class="files-icon-container notebook-link-table"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/01-%20Python%20Tutorial%20Preliminary%20Skills%20and%20Concepts/01%20-%20Machine%20Learning%20for%20Beginners%20Assessment.ipynb">
<img class="files-icon" src="/assets/notebook.svg"></img>
</a>
</td>
<td> Lab </td>
<td>
<a target="_blank" href="https://forms.office.com/e/qXMvfu1TqN" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td rowspan="2">Tue</td>
<td>10:30 - 12:30</td>
<td>2: Linear Models</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/02-%20Regression%20models%20and%20linear%20prediction/ML4NuerScience_Linear_models.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
|
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/notes/02-Linear%20models.pdf">
<img class="files-icon" src="/assets/notes.svg"></img>
</a>
</td>
<td> Lecture </td>
<td>
<a target="_blank" href="https://forms.office.com/e/S2yMFgH6iu" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td>Linear Models - Regression and Classification Models</td>
<td>
<a target="_blank" class="files-icon-container notebook-link-table"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/02%20-%20Linear%20Models%20-%20Regression%20and%20Classification%20Models/02%20%20-%20Linear%20Models.ipynb">
<img class="files-icon" src="/assets/notebook.svg"></img>
</a>
</td>
<td> Lab </td>
<td>
<a target="_blank" href="https://forms.office.com/e/S2yMFgH6iu" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td rowspan="2">Wed</td>
<td>10:30 - 12:30</td>
<td>3: Probability and Information Theory</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/03-%20Probability%20and%20information%20theory/ML4NuerScience_Probability_info_theory.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
|
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/notes/03-Probability%20and%20Information%20Theory.pdf">
<img class="files-icon" src="/assets/notes.svg"></img>
</a>
</td>
<td> Lecture </td>
<td>
<a target="_blank" href="https://forms.office.com/e/s1fhhQZ5CP" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td></td>
<td>
<a target="_blank" class="files-icon-container notebook-link-table"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/03%20-%20Probability%20and%20Information%20Theory/03%20-%20PDF%20CDF.ipynb">
<img class="files-icon" src="/assets/notebook.svg"></img>
</a>
</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td rowspan="2">Thur</td>
<td>10:30 - 12:30</td>
<td>4: Bayesian Models</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/04-%20Bayesian%20models/ML4NuerScience_BayesianModels.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
|
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/notes/04-Bayesian%20models.pdf">
<img class="files-icon" src="/assets/notes.svg"></img>
</a>
</td>
<td> Lecture </td>
<td>
<a target="_blank" href="https://forms.office.com/e/24vTmX3cf5" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td>Bayesian Models</td>
<td>
<a target="_blank" class="files-icon-container notebook-link-table"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/04%20-%20Bayesian%20Models/04%20-%20Probability%20and%20Bayesian%20Theory.ipynb">
<img class="files-icon" src="/assets/notebook.svg"></img>
</a>
</td>
<td> Lab </td>
<td>
<a target="_blank" href="https://forms.office.com/e/24vTmX3cf5" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td rowspan="2">Fri</td>
<td>10:30 - 12:30</td>
<td>5: Ensemble Models and Kernel Based Models</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/05-%20Ensemble%20models%20and%20kernel-based%20models/ML4NuerScience_ensemble%20models_kernel_models.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
|
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/notes/05-Ensemble%20models%20and%20Kernel-based%20models.pdf">
<img class="files-icon" src="/assets/notes.svg"></img>
</a>
</td>
<td> Lecture </td>
<td>
<a target="_blank" href="https://forms.office.com/e/9v3vZGF3Pr" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td>Ensemble Models and Kernel Based Models ★ </td>
<td>
<a target="_blank" class="files-icon-container notebook-link-table"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/05%20-%20Ensemble%20Models%20and%20Kernel%20Based%20Models/05%20-%20SVM%20Decision%20Trees%20and%20Random%20Forest.ipynb">
<img class="files-icon" src="/assets/notebook.svg"></img>
</a>
</td>
<td> Lab </td>
<td>
<a target="_blank" href="https://forms.office.com/e/9v3vZGF3Pr" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
</table>
</div>
<div>
<p>
<div class="table-key">★: Marked labs</div>;
<div class="table-key">☆: Optional formative lab</div>;
<div class="table-key"><img class="files-icon" src="/assets/slides.svg"></img>:
Slides</div>;
<div class="table-key"><img class="files-icon" src="/assets/notes.svg"></img>: Notes</div>;
<div class="table-key"><img class="files-icon" src="/assets/notebook.svg"></img>: Download
lab</div>;
<div class="table-key"><img class="colab-icon"></img>: Open lab
in Colab</div>
</p>
</div>
<div class="schedule-container">
<table class="schedule-table">
<caption>Week 2 - Starting Monday 20th January</caption>
<tr>
<th style='width:8%'>Date</th>
<th style='width:12%'>Time</th>
<th style='width:27%'>Content</th>
<th style='width:12%'>Files</th>
<th style='width:11%'>Activity</th>
<th style='width:10%'>Feedback</th>
<th style='width:20%'>Lecturer</th>
</tr>
<tr>
<td rowspan="2">Mon</td>
<td>10:30 - 12:30</td>
<td>6: Neural Networks</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/06-%20Neural%20Networks/ML4NuerScience_NeuralNets.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
|
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/notes/06-Neural%20Networks.pdf">
<img class="files-icon" src="/assets/notes.svg"></img>
</a>
</td>
<td> Lecture </td>
<td>
<a target="_blank" href="https://forms.office.com/e/u8U3P236me" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td>Neural Networks ♦</td>
<td>
<a target="_blank" class="files-icon-container notebook-link-table"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/06%20-%20Neural%20Networks/06%20-%20Neural%20Networks.ipynb">
<img class="files-icon" src="/assets/notebook.svg"></img>
</a>
</td>
<td> Lab </td>
<td>
<a target="_blank" href="https://forms.office.com/e/u8U3P236me" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>
<div class="no-wrap-chunk">P. Barnaghi</div>,
<div class="no-wrap-chunk">A. Capstick</div>
</td>
</tr>
<tr>
<td rowspan="2">Tue</td>
<td>10:30 - 12:30</td>
<td>7: Convolutional Neural Networks (CNNs)</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/07-%20Convolutional%20Neural%20Networks/ML4NuerScience_CNN.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
|
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/notes/07-Convolutional%20Neural%20Networks%20(CNNs).pdf">
<img class="files-icon" src="/assets/notes.svg"></img>
</a>
</td>
<td> Lecture </td>
<td>
<a target="_blank" href="https://forms.office.com/e/1ivBkvwjQH" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td>Convolutional Neural Networks (CNNs) ★ </td>
<td>
<a target="_blank" class="files-icon-container notebook-link-table"
href="https://github.com/ML4NS/ml4ns.github.io/blob/main/labs/07%20-%20Convolutional%20Neural%20Networks%20(CNNs)/07%20-%20CNNs.ipynb">
<img class="files-icon" src="/assets/notebook.svg"></img>
</a>
</td>
<td> Lab </td>
<td>
<a target="_blank" href="https://forms.office.com/e/1ivBkvwjQH" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td rowspan="2">Wed</td>
<td>10:30 - 12:30</td>
<td>8: Applications and Neuroscience Inspired Machine Learning</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/08-%20Applications%20in%20neuroscience%20and%20neuroscience%20inspired%20models/ML4NuerScience_Applications.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
|
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/notes/08-Applications%20of%20Machine%20Learning%20in%20Neuroscience.pdf">
<img class="files-icon" src="/assets/notes.svg"></img>
</a>
</td>
<td> Lecture </td>
<td>
<a target="_blank" href="https://forms.office.com/e/GjDVdL31iN" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td rowspan="2">Thur</td>
<td>10:30 - 12:30</td>
<td>9: Responsible Machine Learning ♣</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/09-%20Ethical%20considerations%20and%20responsible%20machine%20learning/ML4NuerScience_Ethical_MLpptx.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
|
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/notes/09-Ethical%20considerations%20and%20responsible%20machine%20learning.pdf">
<img class="files-icon" src="/assets/notes.svg"></img>
</a>
</td>
<td> Lecture </td>
<td>
<a target="_blank" href="https://forms.office.com/e/rdiQ5WRvF7" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>
<div class="no-wrap-chunk">P. Barnaghi</div>,
<div class="no-wrap-chunk">L. Rigny</div>
</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td>Use-case Evaluation</td>
<td></td>
<td> Lab </td>
<td></td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td rowspan="2">Fri</td>
<td>10:30 - 12:30</td>
<td> Guest Lecture - Ethical AI: Principles and Practices</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/Ethical%20AI%20-%20Principles%20and%20Practices/Ethical%20AI%20-%20Principles%20and%20Practices.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
</td>
<td> Lecture </td>
<td>
<a target="_blank" href="https://forms.office.com/e/rdiQ5WRvF7" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>N. Fletcher-Lloyd</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td>Debate: Should AI be used to make diagnostic decisions in healthcare?</td>
<td>
</td>
<td> Lab </td>
<td>
<a target="_blank" href="https://forms.office.com/e/rdiQ5WRvF7" class="files-icon-container">
<img class="files-icon" src="/assets/feedback.svg"></img>
</a>
</td>
<td>N. Fletcher-Lloyd</td>
</tr>
</table>
</div>
<div>
<p>
<div class="table-key">★: Marked labs</div>;
<div class="table-key"><img class="files-icon" src="/assets/slides.svg"></img>:
Slides</div>;
<div class="table-key"><img class="files-icon" src="/assets/notes.svg"></img>: Notes</div>;
<div class="table-key"><img class="files-icon" src="/assets/notebook.svg"></img>: Download
lab</div>;
<div class="table-key"><img class="colab-icon"></img>: Open lab
in Colab</div>
</p>
<p>♦: Guest Lecture: Introduction to Pytorch, Alex Capstick</p>
<p>♣: Guest Lecture: Large Language Models for Electronic Healthcare Records (EHR) Data Analysis,
Louise Rigny (Data Scientist, Great Ormond Street Hospital for Children)</p>
</div>
<div class="schedule-container">
<table class="schedule-table">
<caption>Week 3 - Starting Monday 27th January</caption>
<tr>
<th style='width:8%'>Date</th>
<th style='width:12%'>Time</th>
<th style='width:27%'>Content</th>
<th style='width:12%'>Files</th>
<th style='width:11%'>Activity</th>
<th style='width:10%'>Feedback</th>
<th style='width:20%'>Lecturer</th>
</tr>
<tr>
<td rowspan="2">Mon</td>
<td>10:30 - 12:30</td>
<td>Review and Project (Q/A)</td>
<td>
<a target="_blank" class="files-icon-container"
href="https://github.com/ML4NS/ml4ns.github.io/raw/main/slides/10-%20Summary/ML4NuerScience_Summary.pptx">
<img class="files-icon" src="/assets/slides.svg"></img>
</a>
</a>
</td>
<td> Lecture </td>
<td></td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td>Project</td>
<td></td>
<td> Lab </td>
<td></td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td rowspan="2">Tue</td>
<td>10:30 - 12:30</td>
<td>Project</td>
<td></td>
<td> Lab </td>
<td></td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td>Project</td>
<td></td>
<td> Lab </td>
<td></td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td rowspan="2">Wed</td>
<td>10:30 - 12:30</td>
<td>Project</td>
<td></td>
<td> Lab </td>
<td></td>
<td>P. Barnaghi</td>
</tr>
<tr>
<td>13:30 - 16:30</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
</table>
</div>
<div style="margin-top: 2em;">
<h4>March/April 2025</h4>
<p>
In March/April 2025, there will be an optional series on Generative AI and Large Language Models (LLMs)
covering the topics:
</p>
<ul>
<li>
Variational Auto-encoders
</li>
<li>
Transformers and Large Language Models
</li>
<li>
Diffusion Models
</li>
</ul>
<p>Last year's slides and recordings are available <a target="_blank"
href="https://github.com/PBarnaghi/ML4NS">here</a>. </p>
</div>
</div>
<hr class="featurette-divider" id="lectures">
<div class="featurette">
<h3 class="featurette-heading">Lectures</h3>
<div>