---
# System prepended metadata

title: Machine Learning & Data Science discussion group

---

# Machine Learning & Data Science discussion group

The aim of this group is to meet regularly and discuss topics in machine learning and data science as informally or formally as we wish. In order for the group to maintain momentum all participants should be pro-active about taking part and suggest or lead topic discussions. 

Suggestions for group activities,
- Method walkthrough
- Blogpost or article discussion
- Paper discussion
- Method demonstration
- Model/method comparison

## Next Meeting

:::info
- **Where?** See slack updates
- **When?** Wednesday 30th November 2:30pm
- **Who?** Bowen
- **What?** Deep learning methods for object detection


:::


## Next discussion

- **2023-02-01**: Deep learning methods for object detection (Bowen)

## Already discussed


- **2023-01-18:** Speech and Text Language models
- **2022-11-30:** Random Forests and object detection with YOLO (Daniela)
- **2022-11-16:** Deep Mind Research Overview (Robin)
- **2022-11-02:** Basics of supervised neural networks using PyTorch (Nik & Kathryn)
- **2022-10-18:** [Support Vector Machines](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47) (Frances) 
    - https://www.researchgate.net/publication/222699464_Support_vector_machines_versus_multi-layer_perceptrons_for_efficient_off-line_signature_recognition

## Session suggestions for 2023

- ML Frameworks
- Building a reference of large re-trainable models, e.g. Resnet, and their usecases
- Bayesian methods

## Order

You do not need to prepare slides or any formal material but rather should provide the direction for the discussion and let everyone know the level of involvement expected of them.

The following list is alphabetical rather than ordered... once we have looped round everyone we can start again.

- [ ] Bowen
- [x] Daniella
- [x] Frances
- [x] Kathryn
- [x] Nik
- [x] Robin N
- [ ] Robin W

<!---
## Suggested/requested topics

- Support Vector Machines
- Prinicpal Components Analysis
- Autoencoders
- GANs
- Building a reference of large re-trainable models, e.g. Resnet, and their usecases
- How many ways to over-engineer linear regression?
- Matrix multiplication and deep mind
--->

## Example blogs

Links to potential sources of blogpost material

https://towardsdatascience.com

https://thesequence.substack.com

https://simplystatistics.org

https://distill.pub



