This assignment focuses on applying machine learning techniques to analyse and make predictions based on a given dataset representing a real-world scenario. Students will perform data exploration, preprocessing, model selection, training, evaluation, and interpretation, assessing the performance of their chosen models and drawing meaningful conclusions from the results. The submission comprises three components, including a written report detailing the methodology and findings, a reflection discussing challenges and learning experiences, and the Python code written for the analysis. 

The marking criteria are divided into four main areas. Data Preprocessing & Exploration evaluates how well the dataset is handled, including feature analysis and preprocessing steps. Model Development & Evaluation focuses on selecting and training the machine learning models, comparing the performance, and interpreting the results. Report Quality looks at how well the report is structured, the clarity of explanations, and the use of visuals and supporting evidence. Reflection assesses the depth of insights gained from the process and how challenges were addressed. 
        
The marking criteria will be provided on the unit Moodle. Please ensure to read through the marking criteria carefully before submitting your work.

AI ASSESSMENT SCALE - AI PLANNING

You may use Al for planning, idea development, and research. Your final submission should show how you have developed and refined these ideas.

Note: This unit involves programming. For programming assignments you can get examples and ideas from AI, but you must not include code in your program that was generated by AI. You must write the code in your application yourself.  If asked, you must be able to explain any of the code submitted and you must be capable of writing similar code under invigilated, test conditions if required to do so.  In addition, you must only use the language features and techniques covered in the unit.

This assignment focuses on solving an optimisation problem using search strategies to find an efficient solution in a real-world scenario. Students will model the problem, implement the selected algorithm, assess its performance, and analyse the results. The assignment involves problem formulation, algorithm design, implementation, testing, and analysis to evaluate the effectiveness of the solution. The submission comprises three components, including a written report detailing the algorithm and findings, a reflection discussing challenges and learning experiences, and the Python code written for the solution. 

The marking criteria are divided into four areas. Modelling and Algorithm Design assesses how well the problem is formulated, including data structure choices and the design of the selected search algorithm. Implementation evaluates the correctness and efficiency of the implemented algorithm. Testing and Analysis focuses on running the implemented algorithm, capturing results, and analysing its performance based on relevant metrics. Reflection considers insights gained, challenges faced, and potential improvements.

The marking criteria will be provided on the unit Moodle. Please ensure to read through the marking criteria carefully before submitting your work.

AI ASSESSMENT SCALE - AI PLANNING

You may use Al for planning, idea development, and research. Your final submission should show how you have developed and refined these ideas.

Note: This unit involves programming. For programming assignments you can get examples and ideas from AI, but you must not include code in your program that was generated by AI. You must write the code in your application yourself.  If asked, you must be able to explain any of the code submitted and you must be capable of writing similar code under invigilated, test conditions if required to do so.  In addition, you must only use the language features and techniques covered in the unit.

This assignment focuses on applying deep learning techniques to solve a real-world problem. Students will preprocess a dataset, modify pre-trained models, train and evaluate their performance, and analyse the results. The assignment emphasises model selection, data handling, training process design, and performance interpretation to assess how well different approaches work for the given task. This is a group assignment with a written report covering the methodology, results, and analysis. Additionally, each student submits an individual reflection discussing their contributions, challenges faced, and lessons learned.

The marking criteria are divided into five main areas. Data Preparation and Preprocessing assesses the quality of data handling, including preprocessing steps and code clarity. Model Selection and Modification evaluates the choice of pre-trained models and how they are adapted for the task. Model Training and Evaluation focuses on the training process, performance metrics, and interpretation of results. Written Report is graded based on organisation, clarity, content depth, and use of visualisations. Reflection is an individual component assessing insights gained, challenges faced, and contributions to the group work.

The marking criteria will be provided on the unit Moodle. Please ensure to read through the marking criteria carefully before submitting your work.

AI ASSESSMENT SCALE - AI PLANNING

You may use Al for planning, idea development, and research. Your final submission should show how you have developed and refined these ideas.

Note: This unit involves programming. For programming assignments you can get examples and ideas from AI, but you must not include code in your program that was generated by AI. You must write the code in your application yourself.  If asked, you must be able to explain any of the code submitted and you must be capable of writing similar code under invigilated, test conditions if required to do so.  In addition, you must only use the language features and techniques covered in the unit.