Fundamental machine learning of interest:
•Class imbalance learning
•Data stream learning (real-time prediction)
•Ensemble learning algorithms
•Automated machine learning (AutoML)
•Distributed machine learning (Federated learning)
•Data stream learning (real-time prediction)
•Ensemble learning algorithms
•Automated machine learning (AutoML)
•Distributed machine learning (Federated learning)
Machine learning in following application areas:
•Sports data analysis (e.g. GPS data, performance and strategy analysis)
•Automated software engineering (e.g. software defect prediction)
•IoT (e.g. smart cities, energy consumption, environmental monitoring that require handling sensor data.)
•Fault detection
•Automated software engineering (e.g. software defect prediction)
•IoT (e.g. smart cities, energy consumption, environmental monitoring that require handling sensor data.)
•Fault detection
Potential project topics:
Applied projects: Machine learning techniques for
1) Sports data analysis (e.g. performance and strategy analysis)
2) Fake news classification (not NLP focused)
3) Sentiment analysis (good resource here , not NLP focused)
4) Software defect prediction
5) Human activity analysis
6) Energy forecasting
7) IoT applications
8) Environmental challenges
Research-focused projects:
1) Machine learning from multiple/distributed/federated data streams (keywords: data stream learning, concept drift, distributed ML).
2) Data fusion techniques for multiple data streams (keywords: data stream learning, data fusion, distributed ML)
3) Making use of unlabelled data for class imbalance problems (keywords: semi-supervised learning, class imbalance learning)
4) Transfer learning for class imbalance problems (keywords: transfer learning, class imbalance learning)
5) Adaptive transfer learning for data streams (keywords: transfer learning, data stream learning)
6) Data stream clustering (keywords: data stream learning, clustering, non-stationarity)
7) Class evolution in data stream learning (keywords: data stream learning, class evolution)
8) Feature evolution data stream learning (keywords: data stream learning, feature evolution)
9) AutoML for non-stationary data streams (keywords: AutoML, data stream learning, concept drift)
10) AutoML for multi-label classification (keywords: AutoML, multi-label classification)
11) AutoML for class imbalance problems (keywords: AutoML, class imbalance learning)
12) AutoML for distributed data (keywords: AutoML, distributed machine learning, federated learning)
13) Multi-modal federated learning (keywords: multi-modal data, federated learning)
1) Sports data analysis (e.g. performance and strategy analysis)
2) Fake news classification (not NLP focused)
3) Sentiment analysis (good resource here , not NLP focused)
4) Software defect prediction
5) Human activity analysis
6) Energy forecasting
7) IoT applications
8) Environmental challenges
Research-focused projects:
1) Machine learning from multiple/distributed/federated data streams (keywords: data stream learning, concept drift, distributed ML).
2) Data fusion techniques for multiple data streams (keywords: data stream learning, data fusion, distributed ML)
3) Making use of unlabelled data for class imbalance problems (keywords: semi-supervised learning, class imbalance learning)
4) Transfer learning for class imbalance problems (keywords: transfer learning, class imbalance learning)
5) Adaptive transfer learning for data streams (keywords: transfer learning, data stream learning)
6) Data stream clustering (keywords: data stream learning, clustering, non-stationarity)
7) Class evolution in data stream learning (keywords: data stream learning, class evolution)
8) Feature evolution data stream learning (keywords: data stream learning, feature evolution)
9) AutoML for non-stationary data streams (keywords: AutoML, data stream learning, concept drift)
10) AutoML for multi-label classification (keywords: AutoML, multi-label classification)
11) AutoML for class imbalance problems (keywords: AutoML, class imbalance learning)
12) AutoML for distributed data (keywords: AutoML, distributed machine learning, federated learning)
13) Multi-modal federated learning (keywords: multi-modal data, federated learning)
Public data repositories:
UC Irvine Machine Learning Repository
http://archive.ics.uci.edu/ml/
Kaggle datasets
https://www.kaggle.com/datasets
Amazon’s AWS datasets
https://aws.amazon.com/fr/datasets/
Meta portals (they list open data repositories):
http://dataportals.org/
https://data.europa.eu/en/publications/open-data-maturity
http:// quandl.com/
https://www.quandl.com/
Wikipedia’s list of Machine Learning datasets
https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
Quora.com question
https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public
Datasets subreddit
https://www.reddit.com/r/datasets/
Residential house sensor and energy data
https://www.st.ewi.tudelft.nl/~akshay/dred/
https://www.mdpi.com/1996-1073/16/16/6069
https://www.nature.com/articles/s41597-021-00921-y#Tab3
https://data.open-power-system-data.org/household_data/
https://data.openei.org/data_lakes
Facial Recognition Datasets of 2022
www.twine.net/blog/facial-recognition-datasets/
UK Data Service
https://ukdataservice.ac.uk/find-data/browse/
Fintech, A large language model for Finance
https://github.com/The-FinAI/PIXIU
http://archive.ics.uci.edu/ml/
Kaggle datasets
https://www.kaggle.com/datasets
Amazon’s AWS datasets
https://aws.amazon.com/fr/datasets/
Meta portals (they list open data repositories):
http://dataportals.org/
https://data.europa.eu/en/publications/open-data-maturity
http:// quandl.com/
https://www.quandl.com/
Wikipedia’s list of Machine Learning datasets
https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
Quora.com question
https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public
Datasets subreddit
https://www.reddit.com/r/datasets/
Residential house sensor and energy data
https://www.st.ewi.tudelft.nl/~akshay/dred/
https://www.mdpi.com/1996-1073/16/16/6069
https://www.nature.com/articles/s41597-021-00921-y#Tab3
https://data.open-power-system-data.org/household_data/
https://data.openei.org/data_lakes
Facial Recognition Datasets of 2022
www.twine.net/blog/facial-recognition-datasets/
UK Data Service
https://ukdataservice.ac.uk/find-data/browse/
Fintech, A large language model for Finance
https://github.com/The-FinAI/PIXIU