CSMAI21 Artificial Intelligence and Machine Learning

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1. Assignment description

You are required to find a dataset other than the one used in lab sessions and provided in BlackBoard, formulate a problem you want to address with the dataset, build, evaluate and compare three different machine learning models that would address the problem, and draw conclusions and recommendations based on your findings. It is recommended not to use a very simple dataset (preferably of higher complexity than the ones used in lab sessions) as it would give you limited room for presenting the explanatory data analysis, preprocessing, results, and evaluation. One of the three models must be based on a deep learning architecture implemented using the TensorFlow/PyTorch in Python. The submission should include your report, dataset(s) and Python scripts with comments, all included in one zip-file. Your work should be original and produced by you. Copying whole tutorials, scripts or images from other sources is not allowed. Any material you borrow from other sources to build upon should be clearly referenced (use comments to reference in Python scripts); otherwise, it will be treated as plagiarism, which may lead to investigation and subsequent action.

You can use any open data, e.g.:

https://ieee-dataport.org/topic-tags/artificial-intelligence

https://archive.ics.uci.edu/ml/datasets.php

https://www.kaggle.com/datasets

https://data.gov.uk/

Some examples:

Optical Image data:

1. Building Detection and Roof Type Classification https://ieee-dataport.org/competitions/2023-ieee-grss-data-fusion-contest-large-scale-fine-grained-building-classification

2. So2Sat LCZ42 Dataset for land cover classification https://mediatum.ub.tum.de/1483140

3. DOTA: A Large-Scale Benchmark and Challenges for Object Detection in Aerial Images https://captain-whu.github.io/DOTA/dataset.html

Weather and Climate Data:

4. Daily 0900 GMT observations from the university weather stations (back to 1908; there was a site change in 1968): https://metdata.reading.ac.uk/cgi-bin/climate_extract.cgi

5. Five-minute/hourly data from our automatic weather station back to 1 Sept 2014 (has a few missing dates): https://metdata.reading.ac.uk/cgi-bin/MODE3.cgi http://www.met.reading.ac.uk/~sws09a/MODE3_help.html For some further inspiration (visualisation of current data) and information around the above two data sources, check theseresources: https://www.met.reading.ac.uk/weatherdata/wall_display.html https://research.reading.ac.uk/meteorology/atmospheric-observatory/atmospheric-observatorydata/ https://www.ecmwf.int/en/forecasts/charts/catalogue/

6. Daily energy demand over India by state, and (many) meteorological variables of interest averaged over each state (hourly/daily; 2013–present): https://gws-access.jasmin.ac.uk/public/incompass/kieran/kovalchuk/energy-india/

7. Daily observed river discharge at five stations over the Indus and its tributaries, with catchment averaged meteorological and hydrological variables (Jan 2015 to Jan 2021): https://gws-access.jasmin.ac.uk/public/incompass/kieran/kovalchuk/indus-river/

Some notes on the provenance and metadata for the above two data sources:

· River data are from here: http://www.wapda.gov.pk/index.php/river-flow-data

· Energy demand data are scraped from PDF publications on the POSOCO website, e.g.:

https://posoco.in/download/17-05-21_nldc_psp/?wpdmdl=37035

· Catchment- and state-averaged variables were computed using ERA5 data, for which descriptions are available here:

https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview

2. Assignment submission requirements

Items to be submitted on-line through Blackboard Learn include a single zip archive containing:

1) report (PDF or Word file)

2) dataset(s)

3) Python script(s) (PY or IPYNB files)

Front page of the student’s submission

(the following are compulsory)

Module Code:  CSMAI21

Assignment report Title:   Coursework

Date (when the work completed):

Actual hrs spent for the assignment:

We will use information about how long you spent on the assignment when we review and balance coursework between modules for later years. An exact answer is not necessary, but please try to give a reasonable approximation.

Recommended Report Structure

1. Cover page with the title of your project; module code, title, convenor name; your name and student number; date.

2. Abstract (summarise your work and results)

3. Background and problem to be addressed (justify and support with references to literature)

4. Exploratory data analysis (dataset description and visualisation, support with relevant and important figures)

5. Data pre-processing and feature selection

6. Machine learning model N (iterate for each of the three models)

6.1. Summary of the approach (justify why this ML algorithm, support with references to literature)

6.2. Model training and evaluation

6.3. Results and discussion (support with relevant and important tables/figures)

7. Performance measures and evaluation strategies

8. Results comparison across the models built (support with relevant and important tables/figures)

9. Conclusion, recommendations, and future work

10. References.

3. Assessment classification:

The table below shows what is typically expected of the work to obtain a given mark, each part of the assignment is marked according to the following criteria.

4. Marking Scheme: