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Магистратура 2019/2020

Планирование проекта и машинное обучение

Направление: 11.04.02. Инфокоммуникационные технологии и системы связи
Когда читается: 2-й курс, 1 модуль
Формат изучения: с онлайн-курсом
Прогр. обучения: Интернет вещей и киберфизические системы
Язык: английский
Кредиты: 4
Контактные часы: 2

Course Syllabus

Abstract

In this course students will learn : * How to staff, plan and execute a project * How to build a bill of materials for a product * How to calibrate sensors and validate sensor measurements * How hard drives and solid state drives operate * How basic file systems operate, and types of file systems used to store big data * How machine learning algorithms work - a basic introduction * Why we want to study big data and how to prepare data for machine learning algorithms https://www.coursera.org/learn/industrial-iot-project-planning-machine-learning
Learning Objectives

Learning Objectives

  • In this course students will learn : * How to staff, plan and execute a project * How to build a bill of materials for a product * How to calibrate sensors and validate sensor measurements * How hard drives and solid state drives operate * How basic file systems operate, and types of file systems used to store big data * How machine learning algorithms work - a basic introduction * Why we want to study big data and how to prepare data for machine learning algo-rithms The course is based on MOOC “Project Planning and Machine Learning” https://www.coursera.org/learn/industrial-iot-project-planning-machine-learning (Platform - Coursera.org)
Expected Learning Outcomes

Expected Learning Outcomes

  • In this module I share with you my experience in product planning, staffing and execution. You will perform a product tear down and build a bill of materials (BOM) for that product.
  • In this module you will learn about sensors, and in this case, a temperature sensor. You will learn how to calibrate and then validate that a temperature sensor is producing accurate re-sults. We will study how data is stored on hard drives and solid state drives. We will take a brief look at file systems used to store large data sets.
  • In this module we look at machine learning, what it is and how it works. We take a look at a couple supervised learning algorithms and 1 unsupervised learning algorithm. No coding is required of you. Instead I provide working source code to you so you can play around with these algorithms. I wrap up by providing some examples of how ML can be used in the IIoT space.
  • In this module you will learn about big data and why we want to study it. You will learn about issues that can arise with a data set and the importance of properly preparing data prior to a ML exercise.
Course Contents

Course Contents

  • Module 1: Project Planning and Staffing
    Segment 1 - Learning Outcomes, Introduction to a Design Process Segment 2 - Requirements, Scope, Schedule, Resources, Heap Segment 3 - Roles and Responsibilities Segment 4 - Process: Architecture Definition, Design Planning Segment 5 - Process: Architecture Definition, Design Planning 2 Segment 6 - Process: Develop Segment 7 - Process: Verification Segment 8 - Process: Manufacture Segment 9 - Process: Deploy Segment 10 - Process: Validation Segment 11 - Temperature
  • Module 2: Sensors and File Systems
    Segment 1 - Learning Outcomes, Introduction to Thermistors Segment 2 - Terminology: Resolution, Precision, Accuracy, Tolerance Segment 3 - Basic Sensor Circuit Segment 4 - Accuracy Example2 Segment 5 - Calculating Rtherm Segment 6 - Validating Calibration Segment 7 - Filtering Techniques1 Segment 8 - Block, Object and Key-Value Storage Devices Segment 9 - Filesystem Basics Segment 10 - A File on a Hard Drive Segment 11 - A File on a Solid State Drive Segment 12 - File System: NFS Segment 13 - How Big is "Big"? Segment 14 - Traditional File System Bottlenecks Segment 15 - Parallel Distributed File Systems: Hadoop, Lustre
  • Module 3: Machine Learning
    Segment 1 - Learning Outcomes Segment 2 - AI Backgrounder Segment 3 - Machine Learning, What is it? Segment 4 - Machine Learning Schools of Thought Segment 5 - Get the Tools Segment 6 - Categories of Machine Learning Segment 7 - Supervised Learning, Linear Regression Segment 8 - Supervised Learning, Linear Regression Segment 9 - Supervised Learning, Linear Regression Segment 10 - Supervised Learning, Linear Regression Segment 11 - Supervised Learning, Bayes Theorem Segment 12 - Supervised Learning, Naive Bayes Segment 13 - Supervised Learning, Support Vector Machines (SVM) Introduction Segment 14 - Supervised Learning, SVMs12мин Segment 15 - Unsupervised Learning, K-Means Segment 16 - Reinforcement Learning Segment 17 - Supervised Learning, Deep Learning Segment 18 - Rick Rashid, Natural Language Processing Segment 19 - Deep Learning, Hearing Aid Segment 20 - Machine Learning in IIoT Segment 21 - Machine Learning Summary
  • Module 4: Big Data Analytics
    Segment 1 - Learning Outcomes, Definition of Big Data Segment 2 - Importance of Big Data, Characteristics of Big Data Segment 3 - Size of Big Data4 Segment 4 - Introduction to Predictive Analytics Segment 5 - Role of Statistics and Data Mining Segment 6 - Machine Learning, Generalization and Discrimination Segment 7 - Frameworks, Testing and Validating Segment 8 - Bias and Variance in your Data Segment 9 - Out-of-sample Data and Learning Curves Segment 10 - Cross Validation Segment 11 - Model Complexity, Over- and Under-fitting Segment 12 - Processing Your Data Prior to Machine Learning Segment 13 - Good Data, Smart Data6 Segment 14 - Visualizing Your Data Segment 15 - Principal Component Analysis (PCA) Segment 16 - Prognostic Health Management, Hadoop Machine Learning Library Segment 17 - My Example: Predicting NFL Football Winners Segment 18 - Tom Bradicich, Hewlett Packard's Viewpoint on Big Data
Assessment Elements

Assessment Elements

  • non-blocking Текущий контроль
  • non-blocking Экзамен
    В ходе освоения дисциплины формируются следующие компетенции: УК-1, УК-6, УК-7, УК-8, ОПК-3, ПК-21
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.5 * Текущий контроль + 0.5 * Экзамен
Bibliography

Bibliography

Recommended Core Bibliography

  • Layton, M. C. (2012). Agile Project Management For Dummies. Hoboken: For Dummies. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=445936
  • Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media. (HSE access: http://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4698164)
  • Практикум по бизнес-планированию с использованием программы Project Expert: уч. пос. / В.С. Алиев. - 2-e изд., перераб. и доп. - М.: Форум: ИНФРА-М, 2010. - 288 с.: 60x90 1/16. - (Высшее образование). (переплет) ISBN 978-5-91134-394-1 - Режим доступа: http://znanium.com/catalog/product/196396

Recommended Additional Bibliography

  • A Guide to the Project Management Body of Knowledge (PMBOK® Guide), Sixth Edition, 2017. Режим доступа: http://library.books24x7.com/bookshelf.asp
  • Hall, M., Witten, Ian H., Frank, E. Data Mining: practical machine learning tools and techniques. – 2011. – 664 pp.
  • Бизнес-планирование c использованием программы Project Expert (полный курс) : учеб. пособие / В.С. Алиев, Д.В. Чистов. — М. : ИНФРА-М, 2019. — 352 с. + Доп. материалы [Электронный ресурс; Режим доступа: http://www.znanium.com]. — (Высшее образование: Бакалавриат). - Режим доступа: http://znanium.com/catalog/product/1002364
  • Чубукова И.А. - Data Mining - Национальный Открытый Университет "ИНТУИТ" - 2016 - 470с. - ISBN: 978-5-94774-819-2 - Текст электронный // ЭБС ЛАНЬ - URL: https://e.lanbook.com/book/100582