<samp id="e4iaa"><tbody id="e4iaa"></tbody></samp>
<ul id="e4iaa"></ul>
<blockquote id="e4iaa"><tfoot id="e4iaa"></tfoot></blockquote>
    • <samp id="e4iaa"><tbody id="e4iaa"></tbody></samp>
      <ul id="e4iaa"></ul>
      <samp id="e4iaa"><tbody id="e4iaa"></tbody></samp><ul id="e4iaa"></ul>
      <ul id="e4iaa"></ul>
      <th id="e4iaa"><menu id="e4iaa"></menu></th>

      JC3509編程代做、代寫Python程序設計

      時間:2024-03-31  來源:  作者: 我要糾錯



      page 1 of 3
       University of Aberdeen
       South China Normal University
       Aberdeen Institute of Data Science
       & Artificial Intelligence.
       BSc in Artificial Intelligence 2023 – 2024
      **Please read all the information below carefully**
      Assessment I Briefing Document – Individually Assessed (no teamwork)
      Course: JC3509 – Machine Learning Note: This part of assessment accounts for
      30% of your total mark of the course.
      Learning Outcomes
      On successful completion of this component a student will have demonstrated competence in the
      following areas:
      • Ability to identify, prepare, & manage appropriate datasets for analysis.
      • Ability to appropriately present the results of data analysis.
      • Ability to analyse the results of data analyses, and to evaluate the performance of analytic
      techniques in context.
      • Knowledge and understanding of analytic techniques, and ability to appropriately apply
      them in context, making correct judgements about how this needs to be done.
      Information for Plagiarism and Conduct: Your submitted report may be submitted for
      plagiarism check (e.g., Turnitin). Please refer to the slides available at MyAberdeen for more
      information about avoiding plagiarism before you start working on the assessment. Please also read
      the following information provided by the university: https://www.abdn.ac.uk/sls/onlineresources/avoiding-plagiarism/
      In addition, please familiarise yourselves with the following document “code of practice on student
      discipline (Academic)”: https://tinyurl.com/y92xgkq6
      Report Guidance & Requirements
      Your report must conform to the below structure and include the required content as outlined in each
      section. Each subtask has its own marks allocated. You must supply a written report, along with the
      corresponding code, containing all distinct sections/subtasks that provide a full critical and reflective
      account of the processes undertaken.
      Overview
      This assignment tasks you to undertake the full machine learning pipeline, including data handling
      and processing, model construction and training, and evaluation of the developed methods. You are
      tasked to create a neural network to classify data into 3 categories.
      page 2 of 3
      **Please read all the information below carefully**
      The dataset needed to fulfil the requirements of this assessment can be found in MyAberdeen.
      Data:
      This data contains the chemical properties of food product produced by 3 different manufacturers.
      The purpose of this experiment is to explore the relationship between the chemical measures listed
      below and the manufacturer of the food product. The data has 177 records, where the first column
      “Producer” indicates which manufacturer produced the analyses sample. The features of the dataset
      are the following:
      • Producer – Manufacturer of the product (TARGET).
      • Amino_acid – The total percentage content of animo acid.
      • Malic_acid – The percentage content of malic acid.
      • Ash – The measure of ash present in the product.
      • Alc – The alcalinity of ash present.
      • Mg – The measure of magnesium.
      • Phenols – The total measure of phenols.
      • Flavanoids – The measure of flavonoid phenols in the product.
      • Nonflavanoid_phenols – The measure of non-flavonoid phenols in the product.
      • Proanth – Proanthocyanins measure.
      • Colo_int – The color intensity.
      • Hue – Hue of the color.
      • OD – The protein content of the product.
      • Proline – The measure of proline amino acids.
      Objectives:
      The main purpose of employing this data is the following:
      1. Classification: to determine the origin (manufacturer) of the product given analytical
      measurements.
      2. Analysis: to infer which analytical factors would potentially influence the classification of
      the product.
      In order to achieve these objectives, we would like to accomplish the following subtasks using
      machine learning.
      Submission
      Please provide the follow:
      1. A written report explaining the steps undertaken for each task, and the decisions behind each
      choice. You are expected to use machine learning principles to explain your results with
      graphs and/or tables.
      2. A code submission, comprising of ONE commented python file with all code needed to
      replicate the findings in the written report.
      page 3 of 3
      **Please read all the information below carefully**
      Task 1 – Data Preparation (10 Marks)
      Subtasks:
      1. Import the dataset: Please provide a short description of the data provided and import the data
      into your programming environment; provide snippets of code for these purposes.
      2. Preprocess the data: If you did any preprocessing over the data, e.g., normalization, please
      explain it and the reasons why you did that preprocessing; if you did not do any preprocessing,
      also please explain.
      Task 2 – Model Construction (50 Marks)
      You are tasked to build simple fully connected artificial neural network from scratch to classify the
      records into 3 categories (1, 2, or 3).
      You are not permitted to use any machine learning or statistical libraries, you are expected to
      construct the neural network from scratch, i.e. only using core Python and NumPy.
      Subtasks:
      1. Loss function: Select and implement an appropriate loss function, explain why you have
      selected that loss function in relation to the data and the problem.
      2. Network Design: Construct a fully connected neural network with at least one hidden layer.
      Explain your architectural choice and demonstrate by code snippets, tests, and written
      explanation that your code operates as expected. To achieve this, you will need to implement
      both:
      a. The Forward Pass.
      b. The Backward Pass.
      3. Gradient Descent: Update the weights by mini-batch stochastic gradient descent.
      Demonstrate by code snippets, tests, and written explanation that the weights are being
      updated. You can use advanced optimisation tricks if you wish i.e. momentum.
      NOTE: If you are unable to complete the above tasks, you are permitted to use additional libraries
      (i.e. PyTorch) however, this will result in a deduction of 20 marks.
      Task 3 – Model Training (15 Marks)
      Take the model from the previous task and train it on the data you pre-processed in Task 1. Ensure
      that you train your model on a sub-set of the data, holding out a subset for validation.
      Subtasks:
      1. Model Training: Perform training and parameter selection on the training set.
      2. Module Regularisation: Implement a regularisation method, briefly explain (Max 200 words)
      how it works in the context of your code, use code snippets to help.
      3. Model inference: Validate the model by performing inference on the held-out validation data.
      page 4 of 3
      **Please read all the information below carefully**
      Task 4 – Evaluation (25 Marks)
      Evaluate the performance of your trained classifier and employ machine learning principles to
      explain your results with graphs and/or tables. In addition, perform some analyses on the trained
      model to better understand which analytical factors would potentially influence the classification of
      the product.
      Subtasks:
      1. Present Results: Present the results of your classifier via appropriate metrics for the problem
      statement.
      2. Plot: Plot the loss curve for training and validation, answer the following questions:
      a. What does your loss curve tell you?
      b. Are you observing any overfitting or underfitting?
      c. Does the addition of regularisation help?
      3. Explain Results: Explain the results from the previous subtasks in context of the problem
      statement/setting.
      Marking Criteria
      • Depth and breadth of knowledge.
      • Technical details of formalisation, implementation and pseudo-code.
      • Communication skills (clear, technical contents and sound reasoning)
      • Structure of document.
      Submission Instructions
      You should submit a PDF version of your report and the accompanying code to the Codio
      environment. For the deadline of this assessment, please check it on MyAberdeen. The name of the
      PDF file should have the form “JC3509_Assessment1 _< your Surname>_<your first
      name>_<Your Student ID>”. For instance, “JC3509_Assessment1_Smith_John_4568985.pdf”,
      where 4568985 is your student ID.
      Any questions pertaining to any aspects of this assessment, please address them to the course
      請加QQ:99515681  郵箱:99515681@qq.com   WX:codehelp








       

      標簽:

      掃一掃在手機打開當前頁
    • 上一篇:CHC5223代寫、Java/c++編程設計代做
    • 下一篇:代寫CSci 4061、c/c++,Java程序代做
    • 無相關信息
      昆明生活資訊

      昆明圖文信息
      蝴蝶泉(4A)-大理旅游
      蝴蝶泉(4A)-大理旅游
      油炸竹蟲
      油炸竹蟲
      酸筍煮魚(雞)
      酸筍煮魚(雞)
      竹筒飯
      竹筒飯
      香茅草烤魚
      香茅草烤魚
      檸檬烤魚
      檸檬烤魚
      昆明西山國家級風景名勝區
      昆明西山國家級風景名勝區
      昆明旅游索道攻略
      昆明旅游索道攻略
    • 福建中專招生網 NBA直播 短信驗證碼平臺 幣安官網下載 WPS下載

      關于我們 | 打賞支持 | 廣告服務 | 聯系我們 | 網站地圖 | 免責聲明 | 幫助中心 | 友情鏈接 |

      Copyright © 2025 kmw.cc Inc. All Rights Reserved. 昆明網 版權所有
      ICP備06013414號-3 公安備 42010502001045

      主站蜘蛛池模板: 中文字幕人妻无码专区| 亚洲成av人片在线观看无码不卡| 伊人久久综合精品无码AV专区| 无码A级毛片日韩精品| 国产V片在线播放免费无码| 人妻丰满熟妇AV无码区乱| 特级无码毛片免费视频| 亚洲AV色吊丝无码| 国产精品无码一区二区三级| 国产成人无码精品久久久小说| 无码H肉动漫在线观看| 一本无码中文字幕在线观| 亚洲另类无码专区首页| 亚洲国产成人无码av在线播放| 日韩人妻无码精品无码中文字幕| 亚洲成AV人在线播放无码| 成人无码视频97免费| 中文国产成人精品久久亚洲精品AⅤ无码精品 | 中文无码日韩欧免费视频| 亚洲中文字幕久久精品无码VA| 国产精品无码无需播放器| 久久久久亚洲?V成人无码| 无码毛片内射白浆视频| 无码一区18禁3D| 在线A级毛片无码免费真人| 精品国产v无码大片在线观看| 亚洲?v无码国产在丝袜线观看| 岛国av无码免费无禁网站| 久久精品无码专区免费青青| 无码中文字幕日韩专区| 精品一区二区三区无码免费视频| 亚洲午夜无码久久久久| 国产成A人亚洲精V品无码| 中文字幕丰满乱孑伦无码专区 | 久久久久久久久无码精品亚洲日韩 | 狠狠精品干练久久久无码中文字幕| 色窝窝无码一区二区三区成人网站| 亚洲AV永久纯肉无码精品动漫 | 久久人妻无码中文字幕| 亚洲精品无码专区| 尤物永久免费AV无码网站|