Machine Learning Practical (MLP) Lectures 2021-22
Machine Learning Practical (MLP) Lectures 2021-22
The Machine Learning Practical (MLP) course for 2021-22 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.
Playlists:
Single-Layer Deep NNs Deep NNs CNNs RNNs
Lecture 01 Lecture 03 Lecture 05 Lecture 07 Lecture 09
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Machine Learning Practical 2023-24 Live Lecture session for Week 10 of Semester 1. From Artificial Intelligence to Reinforcement Learning from Human Feedback
MLP 23-24 Week 10 Live Session - From AI to RLHF
From Pavlos Andreadis
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Machine Learning Practical Week 9, Part 1 of 2 on Recurrent Neural Networks. Live lecture from 2022-23.
Machine Learning Practical - Week 9 (Live 2022-23)
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 08, Clip 04 / 05.
MLP Lecture 08 - Clip 04 - Implementing CNNs
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 08, Clip 03 / 05.
MLP Lecture 08 - Clip 03 - Gradients w.r.t. Inputs
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 08, Clip 02 / 05.
MLP Lecture 08 - Clip 02 - Gradients w.r.t.…
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 08, Clip 01 / 05.
MLP Lecture 08 - Clip 01 - Dilated Convolutions
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 08, temporary 2019-20 live version.
MLP Lecture 08 - Convolutional Neural Networks…
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 07, Clip 09 / 09.
MLP Lecture 07 - Clip 09 - a Short History of CNNs
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 07, Clip 08 / 09.
MLP Lecture 07 - Clip 08 - Learning Image Kernels
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 07, Clip 07 / 09.
MLP Lecture 07 - Clip 07 - Pooling/Subsampling
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 07, Clip 06 / 09.
MLP Lecture 07 - Clip 06 - Multiple Feature Maps
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 07, Clip 05 / 09.
MLP Lecture 07 - Clip 05 - Receptive Field
From Pavlos Andreadis
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MLP Lecture 07 - Clip 04 - Padding; Stridding 13 of 15
MLP Lecture 07 - Clip 04 - Padding; Stridding
Machine Learning Practical (MLP) Lecture 07, Clip 04 / 09.MLP Lecture 07 - Clip 04 - Padding; Stridding
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 07, Clip 03 / 09.
MLP Lecture 07 - Clip 03 - Example
From Pavlos Andreadis
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Machine Learning Practical (MLP) Lecture 07, Clip 02 / 09.
MLP Lecture 07 - Clip 02 - What are Convolutions?
From Pavlos Andreadis
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