1 COMP1804Applied Machine LearningFaculty Header ID:Contribution100% of courseCourse LeaderDr. Dimitrios KolliasCOMP1804 CourseworkDeadline Date:16/04/2021This coursework should take an average student who is up-to-date with the lecturesand the labs approximately 50 hoursFeedback and grades are normally made available within 15 working days of thecoursework deadlineLearning Outcomes:1. Demonstrate understanding and ability to building and pre-processing data forMachine Learning (ML) algorithms.2. Critically evaluate the merits and limitations of machine learning approaches andalgorithms and evaluate the choice of algorithms for specific real-world contexts andrequirements.3. Apply ML algorithms to a selected real-word problem in practice and understand theprocesses involved in their deployment.4. Demonstrate ability to evaluate the performance of ML algorithms in the context ofthe specific real-world problem. Plagiarism is presenting somebody else’s work as your own. It includes: copyinginformation directly from the Web or books without referencing the material;submitting joint coursework as an individual effort; copying another student’scoursework; stealing coursework from another student and submitting it as yourown work. Suspected plagiarism will be investigated and if found to haveoccurred will be dealt with according to the procedures set down by theUniversity. Please see your student handbook for further details of what is / isn’tplagiarism.All material copied or amended from any source (e.g. internet, books)must be referenced correctly according to the reference style you areusing.Your work will be submitted for plagiarism checking. Any attempt tobypass our plagiarism detection systems will be treated as a severeAssessment Offence.2Coursework Submission Requirements• An electronic copy of your work for this coursework must be fully uploadedon the Deadline Date using the link on the coursework Moodle page forCOMP1804.• For this coursework you must submit 7(+2) separate files:– A single pdf file named ‘report.pdf’ which will be the written report; thewritten report must have a maximum limit of 3500 words– A single zip file named ‘model.zip’ which will be the trained modelweights in TensorFlow format– A single csv file named ‘annotations.csv’ which will include theannotations for the given unlabelled data. Regarding the format:o the first line should be:image_name,wrinkles,freakles,glasses,hair_color,hair_topo next lines should list the image name & correspondingannotations, e.g.:flick_23.jpg,1,1,2,5,2photobucket_123.jpg,0,1,0,3,1pexels_0.jpg,0,0,1,6,0– A single ipython notebook file named ‘test_code.ipynb’ which will be thesource code for testing the developed model on new images (this codeshould take as input new images’ file locations and output a file with thepredictions of the model in the same format as the above describedannotations.csv file)– A single ipython notebook file named ‘training_code.ipynb’ which will bethe source code for training the model on images (this code should readthe images and their annotations from a file having the same format asthe above described annotations.csv file)– A text file named ‘requirements.txt’ containing the libraries required torun the above described test_code.ipynb, if there are any– A single video file named ‘demo.mp4’ which will be the demo showingthe developed model predicting the facial attributes on a few (3-10)unseen images (not belonging to the provided dataset)– If the provided annotation tool was used: the video that you created forthe annotation tool named ‘video.mov’ (.mp4/.avi or any other extension)– If the provided annotation tool was used: the text file created by theprovided script (‘merge_images_to_video.py’); the text file should benamed ‘image_names.txt’In general, any text in the document must not be an image (i.e. must not bescanned) and would normally be generated from other documents (e.g. MSOffice using “Save As .. PDF”). An exception to this is hand writtenmathematical notation, but when scanning do ensure the file size is notexcessive.• There are limits on the file size (see the relevant course Moodle page).• Make sure that any files you upload are virus-free and not protected by apassword or corrupted otherwise they will be treated as null submissions.• Your work will not be printed in colour. Please ensure that any pages with3colour are acceptable when printed in Black and White.• You must NOT submit a paper copy of this coursework.• All courseworks must be submitted as above. Under no circumstances canthey be accepted by academic staffThe University website has details of the current Coursework Regulations,including details of penalties for late submission, procedures for ExtenuatingCircumstances, and penalties for Assessment Offences.See http://www2.gre.ac.uk/current-students/regsDetailed Coursework SpecificationDesigning a machine learning solution requires considering several aspects of theproblem, the availability of data and corresponding annotations, nature of theproblem addressed, methodology choice, evaluation among others. It is importantfor our students to be up to date with current practices and Machine Learningtechniques used in the modern software that drives many computers and devicestoday and be familiar with their strengths and limitations. It is of equal importancefor our students to familiarize with the whole data processing and evaluationpipeline enabling successful implementation of Machine Learning techniques.Adding these skills to their portfolio will increase the employability of our graduatesand will help them to aim for higher paying jobs in industry, as well as academia.The task is to implement a ML solution for facial attribute recognition/classification.The facial attributes are:– wrinkles (binary: has/does_not_have), class 0: does_not_have, class 1: has– freakles (binary: has/does_not_have), class 0: does_not_have, class 1: has– glasses (3 values: do_not_wear/wear_normal/wear_sunglasses), class 0:does_not_wear, class 1: wear_ normal, class 2: wear_sunglasses– hair_color (9 values: brown/black/gray/blond/red/white/mixed/other), class 0: brown,class 1: black, class 2: gray, class 3: blond, class 4: red, class 5: white, class 6: mixed,class 7: other, class 8: not_visible– hair_top (4 values: bald or shaved, has_few_hair, has_thick_hair), class 0: bald orshaved, class 1: has_few_hair, class 2: has_thick_hair, class 3: not_visibleA related unlabelled dataset will be distributed to each student. The student shouldimplement the whole procedure for designing a ML approach for solving the problem.Tasks:1. Practical Assignment (65 Marks) (complete training and testing code, trainedmodel weights and annotations to replicate the results). The source code mustbe error free (i.e. no debugging necessary to run). The assignment includes:o Annotation and pre-processing: this should include data labelling, data splitting,4generating statistics and data pre-processing (20 marks; it will be possible toobtain 10 more marks if an additionally provided dataset is also labelled andused; this additional dataset to be provided upon request)o ML methodology: an appropriate ML method should be used that has a coherentimplementation and a sound pipeline, without any errors (25 marks)o Experimental results: this should include evaluation of the ML algorithmperformance with metrics and figures/tables. The method’s performance willadditionally be evaluated by the teaching staff on a held-out test set (20 marks)2. Written Report (35 Marks):• Document in IEEE conference format (Use template available online:https://www.ieee.org/conferences/publishing/templates.html)• Should include references (citing other work) where appropriate (whenimages, data, code, or any other resources have been used from othersources)• Document structure:o Introduction: Introduce the problem to be solved (motivation,expectances, goals, implications)o Related work: Short survey of other existing/state-of-the-art work onthe studied problemo Dataset preparation: Describe data collection, processing andpartitioning (sources, augmentation techniques, training setup, …)o ML method: Motivate method-related choices, explain how themethod works (motivate decisions during training, …)o Evaluation: Evaluate, present, analyse and explain methodperformance (highlight the pros and cons of it; focus on both goodand bad: an unsatisfactory result, if well explained, can help othersfocus on working solutions; include a demo)o Future work: Reflections on how your work could be extended in thefuture; what addition can be made to ito References: All existing works and resources (code/images/etc) youused or talked about in your report must be cited properlyDeliverables:An admissible coursework submission needs to include:• All coursework submission requirements as specified in the CourseworkSubmission Requirements section above should be uploaded by theDeadline Date using the link on the coursework Moodle page for COMP1804.Grading CriteriaFor a distinction (mark over 70) the following is required:• An excellent implementation, showing a whole system with all requirements5implemented; all components are working and provide a very good result.• An excellent report and demonstrating a good understanding ofmotivating,building and evaluating a working machine learning application.Note: In order to be eligible for very high marks (80 & over) you will need to have:• An innovative implementation, showing all requirements are implementedto a higher standard. The components should be working properly andprovide an excellent result; potentially extra credit features implemented.• An outstanding report with excellent portfolio, demonstrating a thoroughunderstanding of motivating, building and evaluating a working machinelearning application.For a mark in the range 60 to 69 the following are required:• A good implementation, showing a system with no errors and with a soundtraining pipeline and annotation leading to good evaluation-results.• A good report demonstrating a good understanding of motivating, buildingand evaluating a working machine learning application.For a mark in the range 50 to 59 the following are required:• An implementation showing a reasonable system with at least followingminimum requirements implemented: basic annotation, flawless MLimplementation, reasonable evaluation-results.• An adequate report showing some understanding of motivating, building andevaluating a working machine learning application.For a mark below 50:• A system that fails to implement the minimum requirements, including basicannotation, flawless ML implementation, reasonable evaluation-results.• An unsatisfactory report showing little understanding of motivating, building andevaluating a working machine learning application.6Assessment CriteriaThe practical assessment = 65 MarksMarks will be given for:• Features implemented.o The extent to which a successful annotation, pre-processing, trainingand evaluation pipeline was implemented will have an importanteffect on your overall mark.• The quality of the system you produce.o Credit will be given for excellent robust design, complexity of theimplementation, components that are working without giving any errorsand reliably producing good result; possible enhancements to thesystem.The report = 35 MarksMarks will be given for:• Critical understanding of relevant concepts, reading and referencing relatedpapers, appropriate explanation and discussion.• Quality of the report:Are all the required sections included and completed properly? Is the report clear,well formatted and easy to read? Does it have a logical structure? Does it have adiscussion on design decisions? Is the evaluation realistic, does it show that youhave really thought about your system and how you went about developing it.
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