The algorithm behind the k-nearest neighbour classifier dates back to the early days of computer science. Its simplicity, amongst other properties, make it a great introduction to machine learning algorithms.
In the workshop, we'll implement a k-NN classifier in Python (you can use any language you like, but the material will use Python), including training and testing against a data set using k-fold cross validation. If any of those terms are new to you, don't worry! We'll assume no previous knowledge and start from the basics.
There's a Github repository available at https://github.com/ShefML/dojo-knn with the workshop material - feel free to get started before the workshop!
There's plenty of reasons to come along if you're an ML expert too. From exercising your coaching skills with other participants to exploring effectiveness and performance optimisations, you can use the time as you like.
Where and When? We're looking to open doors at 6:30pm. If you're late, or you need to leave early, that's totally fine.
What do I need? You'll need a working installation of your chosen language, and you'll need to know how to do things like generate random numbers. If you're not sure which language to use, Python 3 might be the easiest choice, and a strong choice for ML generally - and it might already be installed on your computer (http://docs.python-guide.org/en/latest/starting/installation/).
Is it beginner-friendly? Yes.
But I'm an expert? It'd be great if you could speak and help coach beginners, we'll make sure there's space and opportunity to meet other more experienced people and play with more advanced topics.
The algorithm behind the k-nearest neighbour classifier dates back to the early days of computer science. Its simplicity, amongst other properties, make it a great introduction to machine learning algorithms.
In the workshop, we'll implement a k-NN classifier in Python (you can use any language you like, but the material will use Python), including training and testing against a data set using k-fold cross validation. If any of those terms are new to you, don't worry! We'll assume no previous knowledge and start from the basics.
There's a Github repository available at https://github.com/ShefML/dojo-knn with the workshop material - feel free to get started before the workshop!
There's plenty of reasons to come along if you're an ML expert too. From exercising your coaching skills with other participants to exploring effectiveness and performance optimisations, you can use the time as you like.
Where and When? We're looking to open doors at 6:00pm, and start the workshop at 6:30pm. If you're late, or you need to leave early, that's totally fine.
What do I need? You'll need a working installation of your chosen language, and you'll need to know how to do things like generate random numbers. If you're not sure which language to use, Python 3 might be the easiest choice, and a strong choice for ML generally - and it might already be installed on your computer (http://docs.python-guide.org/en/latest/starting/installation/).
Is it beginner-friendly? Yes.
But I'm an expert? It'd be great if you could speak and help coach beginners, we'll make sure there's space and opportunity to meet other more experienced people and play with more advanced topics.
The algorithm behind the k-nearest neighbour classifier dates back to the early days of computer science. Its simplicity, amongst other properties, make it a great introduction to machine learning algorithms.
In the workshop, we'll implement a k-NN classifier in Python (you can use any language you like, but the material will use Python), including training and testing against a data set using k-fold cross validation. If any of those terms are new to you, don't worry! We'll assume no previous knowledge and start from the basics.
There's plenty of reasons to come along if you're an ML expert too. From exercising your coaching skills with other participants to exploring effectiveness and performance optimisations, you can use the time as you like.
Where and When? We're looking to open doors at 6:00pm, and start the workshop at 6:30pm. If you're late, or you need to leave early, that's totally fine.
What do I need? You'll need a working installation of your chosen language, and you'll need to know how to do things like generate random numbers. If you're not sure which language to use, Python 3 might be the easiest choice, and a strong choice for ML generally - and it might already be installed on your computer (http://docs.python-guide.org/en/latest/starting/installation/).
Is it beginner-friendly? Yes.
But I'm an expert? It'd be great if you could speak and help coach beginners, we'll make sure there's space and opportunity to meet other more experienced people and play with more advanced topics.
The algorithm behind the k-nearest neighbour classifier dates back to the early days of computer science. Its simplicity, amongst other properties, make it a great introduction to machine learning algorithms.
In the workshop, we'll implement a k-NN classifier in Python (you can use any language you like, but the material will use Python), including training and testing against a data set using k-fold cross validation. If any of those terms are new to you, don't worry! We'll assume no previous knowledge and start from the basics.
There's plenty of reasons to come along if you're an ML expert too. From exercising your coaching skills with other participants to exploring effectiveness and performance optimisations, you can use the time as you like.
Where and When? We're looking to open doors at 6:00pm, and start the workshop at 6:30pm. If you're late, or you need to leave early, that's totally fine. Venue TBC, but it'll be Sheffield City Centre.
What do I need? You'll need a working installation of your chosen language, and you'll need to know how to do things like generate random numbers. If you're not sure which language to use, Python 3 might be the easiest choice, and a strong choice for ML generally - and it might already be installed on your computer (http://docs.python-guide.org/en/latest/starting/installation/).
Is it beginner-friendly? Yes.
But I'm an expert? It'd be great if you could speak and help coach beginners, we'll make sure there's space and opportunity to meet other more experienced people and play with more advanced topics.
New!Title changed:Cantor Building, Room 9223Address changed:153 Arundel StreetAddress postcode changed:S1 2NUPosition on map changed:Yes (53.377346,-1.4676386)Area changed:YesCountry changed:YesEdited at by brabster