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У листопаді відбулись професійні лекції для студентів

Листопад виявився насиченим на професійні лекції для студентів факультету електроніки та комп’ютерних технологій ЛНУ ім. І. Франка. Щосереди з 8 листопада було організовано 4 вступні лекції від ІТ-спеціалістів (з ІТ-компаній Indeema, Sciforce та Cypress) по кожному з напрямків навчання програми «Data Science&Intelligent Systems».

Лекції проведились з метою допомогти студентам навчальної програми зробити свій вибір щодо майбутнього напрямку навчання (починаючи з другого курсу), а поки 45 першокурсників навчаються за спільною програмою.

  • 8 листопада. Лекція “IoT introduction. Technologies overview”

Лекцію по напрямку Internet of Things (Інтернет речей) провів Володимир Шевчик, CEO компанії “Indeema Software Inc.“. Лектор зазначив: “В університетські часи нам бракувало практичних знань від спеціалістів, які знають, розуміють та використовують знання в роботі. Більшість занять були теоретичними. А мене завжди цікавила практична сторона. Саме тому, проводячи дану лекція, я хочу віддати студентам те, що сам би хотів отримати під час навчання”.

Матеріали лекції завантажуйте за посиланням : IoT-introduction (8.11).

Нижче можна переглянути відеосюжет Franko TV – студентське телебачення про першу додаткову лекцію.

В кінці лекції Володимир анонсував проведення серії з трьох практичних воркшопів «Інтернет речей» у грудні. Усі бажаючі зможуть попрактикуватися у створенні «розумних речей». Для участі необхідна реєстрація.

 

  • 15 листопада. Лекція “Low-cost high-performance Smart Solutions”

Лекцію по напрямку Smart Solutions&Intelligent Systems (Розумні рішення та системи) провів Володимир Бігдай,  Sr Staff Systems Engineer у Cypress Semiconductor Corp. Під час виступу лектор розповів про основні проблеми і задачі, які вирішуються при побудові embedded-систем і розумних рішень: “Основна рушійна сила прогресу в напрямку embedded-систем – це ціна. Тиражі є мільйони, сотні мільйонів, мільярди. Якщо зекономити 1$ на одній деталі, то загалом можна зекономимо мільйони, сотні мільйонів $. Ціна виробництва і проектування – це ключова річ для більшості речей. Якщо ми говоримо про військову сферу, авіацію, медицину, тоді ціна може стати на другий план, адже потрібно використовувати сертифіковані мікросхеми і софт (які мають витримувати певні температурні межі, іонізуючу радіацію і т.д). Найнижчий рівень сертифікації має звичайна користувацька електроніка, вищий у автомобільної електроніки, потім – медична, і найвищий рівень – у військової та космічної електроніки. 

 

Наступними важливими аспектами є safe&secure, гнучкість та час. Дуже цінується користувачами, коли ти увімкнув систему і вона працює, нічого додатково не потрібно інталювати та завантажувати. Наприклад, автомобіль Tesla не випускається мільйонними тиражами, тому для неї немає сенсу робити спеціалізований мікроконтролер. В такому випадку використовують мікроконтролери загального користування (гнучкі) і підганяють під свої потреби. З іншого боку, якщо маємо мільйонні чи мільярдні тиражі, тоді вже роблять спеціальні обрізані версії мікроконтролерів, в яких є виключно той функціонал, який потрібен.

Останнім важливим аспектом є time-to-market: наскільки багато йде часу, щоб розробити, спроектувати і відправити на виробництво. Якщо інша компанія-конкурент чи хтось інший може це робити швидше, то вона матиме перевагу.”

За посиланням переглядайте матеріали лекції: Low-cost high-performance Smart Solutions (15.11).
Демонстрація деяких “розумних систем та рішень”

 

  • 22 листопада. Лекція “DS in Healthcare”

Лекцію по напрямку Data Analysis (Аналітика даних) провів Юрій Хома, Data Science Lead у SciForce. Спочатку лектор зауважив: “Коли ви працюєте з даними, то найперше питання, яке потрібно ставити: що корисного можна витягнути з цих даних? І якщо можна, то що саме?”.

Якщо говорити про аналітику даних в медичній сфері, на думку лектора, найбільш розвиненими галузями є: 1. Біоінформатика: аналіз геному, персоналізовані ліки і т.д. В цій галузі є багато невирішених завдань, адже багато речей, які з нами стаються, залежать від нашої генетики.  Зробити висновок з генів про те, чи є чи нема схильності до певних хворіб – дуже складно, адже геном не розшифрований.
2. Різні діагностики. Є об’єктивні перешкоди, бо люди довіряють більше медикам і ще немає такого сплеску, хоча ведуться серйозні роботи і можливо через якийсь час це зміниться.
3. Електронна медична карта пацієнтів. Медичну історію пацієнтів в багатьох країнах є електронному вигляді. Це фактично база даних, в які внесені історії пацієнтів (які ліки приймав, якими хворобами хворів, які були травми і т.д.). Кожна хвороба, ліки кодуються певним кодом. В Україні починають робити кроки в цьому напрямку.

В кінці лекції лектор виділив такі задачі data science в healthcare: “порівняння ефективності ліків, ефективності лікування, можуть бути і порівняння ефективності клінік. До цифр потрібно підходити дуже обережно, адже цифри можуть помилятись. І найважливіше, data science – бізнес-орієнтована галузь. Фактично це є перетин математики, програмування і предметної галузі. В нашому випадку предметна галузь – це медицина”.

За посиланням переглядайте матеріали лекції: Data Science in Healthcare (22.11).

 

  • 29 листопада. Лекція “AI applications in IoT and robotics”

Лекцію по напрямку Machine Learning&Artificial Intelligence (Машинне навчання та штучний інтелект) провів Юрій Хома, Data Science Lead у SciForce.На початку лектор зазначив: “Вибух в галузі Штучного інтелекту відбувся не так давно, близько 2009-2012 років (за різними оцінками). З цього часу і почались серйозні зрушення. З’явились нові алгоритми, які дозволили зробити серйозний прорив в комерційних застосуваннях і тому, спаду ми поки не спостерігаємо. Відмінність від старих етапів є такою, що зараз штучний інтелект знайшов широке застосування”.

Визначення машинного навчання: “Алгоритми, які ми використовуємо в штучному інтелекті носять назву “нейронних мереж”. Фактично потрібно сформувати дерево рішень, але справа в тому, що його потрібно продумати вручну. Коли ми говоримо про “машинне навчання”, тут ключове слово “навчання” – система вчиться сама, тобто сама формує це дерево рішень. Вона сама приймає рішення, звертаючи увагу на попередні рішення і на основі того пробує навчитись, точно так само як ми – методом проб і помилок”.

Під час лекції слухачі можли переглянути декілька відео проектів, в яких участь брав лектор.

За посиланням переглядайте матеріали лекції: Лекція AI applications in IoT and robotics (29.11)

 

  • 30 листопада. Інтерактивне заняття “Team Work” 

Під час інтерактивного заняття “Team Work” студенти дізнались що таке команда, як вона функціонує, як бути хорошим командним гравцем, а також змогли попрактикувати здобуті теоретичні знання в командних іграх “Банкір. Підприємець. Депутат”, “Соціальні лінощі” та “Є контакт!”. Заняття проводила Софії Невойт,  практичний психолог, арт-терапевт, викладач занять «Soft Skills» у LITS4teens Lviv IT School (LITS).

Відвідати лекції та інтерактивне заняття могли студенти навчальної програми “Data Science & Intelligent Systems at LNU” та усі бажаючі.

11.01.2018

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