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mds:ddam:start [03/11/2020 alle 23:51 (19 mesi fa)]
Roberto Trasarti [Registro delle Lezioni - Lessons log (in english)]
mds:ddam:start [30/09/2021 alle 08:25 (8 mesi fa)] (versione attuale)
Roberto Trasarti [Registro delle Lezioni - Lessons log (in english)]
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 ===== Orario Lezioni - Lessons Schedule ===== ===== Orario Lezioni - Lessons Schedule =====
 ^ Giorno ^ Ora ^ Luogo ^ ^ Giorno ^ Ora ^ Luogo ^
-|Monday|14:15 - 16:00| On-line+|Monday|14:15 - 16:00 |C1 & Microsoft Teams 
-|Wednesday|14:15 - 16:00| On-Line|+|Thursday|11:15 - 13:00 |M1 & Microsoft Teams |
  
-**Prima Lezione - First Lecture: 16/09/2020** +**Prima Lezione - First Lecture: 13/09/2021**
- +
-Link teams classroom: https://teams.microsoft.com/l/channel/19%3a4500e29b9644403bb4333a24473979e9%40thread.tacv2/Generale?groupId=1a3cfb99-a6dd-467c-ad9f-e745714b1f98&tenantId=c7456b31-a220-47f5-be52-473828670aa1+
  
 ===== Scopo del Corso - Aim of the course ===== ===== Scopo del Corso - Aim of the course =====
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 === Syllabus: === === Syllabus: ===
- 
  
   * Motivations: What is and Why Distributed Data Mining is needed in a Big Data Scenario   * Motivations: What is and Why Distributed Data Mining is needed in a Big Data Scenario
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   * Data Analysis with Spark    * Data Analysis with Spark 
   * Data Mining and Machine Learning with Spark    * Data Mining and Machine Learning with Spark 
 +  * Spark SQL
 +  * Spark Streaming
   * Example on how to prepare a project     * Example on how to prepare a project  
  
  
  
-===== Registro delle Lezioni - Lessons log (in english=====+(Slides and materials on Microsoft Teams channel: https://bit.ly/3A8dH5V)
  
-(Slides and materials: in the teams classroom repository) 
-  * Wednesday 16/09 - Parallel Computation basic concepts, Map Reduce I (lecture 1)  
-  * Wednesday 23/09 - Map Reduce II, Access to Servers (lecture 2) 
-  * Wednesday 30/09 - Map Reduce III, Spark I (lecture 3) 
-  * Monday 5/10 - Spark II (lecture 4) 
-  * Wednesday 7/10 - Exercises (lecture 5) 
-  * Monday 12/10 - Spark SQL (lecture 6) 
-  * Wednesday 14/10 - MLlib I (lecture 7) 
-  * Monday 19/10 - MLlib II + Exercises (lecture 8)  
-  * Wednesday 21/10 - Exercises/Example Projects (lecture 9) 
-  * //Break!// 
-  * Wednesday 4/11 - Mlib + Spark Streaming (lecture 10) 
-  * Monday 9/11 - Datasets presentation from Kaggle (Students presentation) 
-  * Wednesday 11/11 -  Project objectives presentation (Students presentation) 
-  * Monday 16/11 - Open Lab 
-  * Wednesday 18/11 - Open Lab 
-  * Monday 23/11 - Open Lab 
-  * Wednesday 25/11 - Open Lab 
-  * Monday 30/11 - Open Lab 
-  * Wednesday 2/12 - Open Lab 
-  * Monday 7/12 - Project Alignment II 
      
 ===== Metodo di valutazione - Examination process ===== ===== Metodo di valutazione - Examination process =====
-Students groups made of 3 o 4 students (max) develop a project (report + short slide presentation); \\ +Students groups made of 3 o 4 students (max) develop a project (report + python notebooks); \\
-Every student perform individual test (multiple choices). \\ +
-Final grade will result from a combination of project mark (70% of the final grade) and individual test mark (30%).+
  
 ==== Laboratory ==== ==== Laboratory ====
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 ===== Software & Links ===== ===== Software & Links =====
-  * Python website: http://www.python.it/download/ (Install the 2.x. Do not install 3.x). Instructions [[https://goo.gl/yBRjkG]]+  * Python website: http://www.python.it/download/ (3.x version). Instructions [[https://goo.gl/yBRjkG]]
   * Installing Hadoop single node on your machine (without VM): https://goo.gl/KGME9t (Linux/OS) https://goo.gl/7Bkcnr (Win)   * Installing Hadoop single node on your machine (without VM): https://goo.gl/KGME9t (Linux/OS) https://goo.gl/7Bkcnr (Win)
   * Spark http://spark.apache.org/downloads.html (Can be installed without hadoop)   * Spark http://spark.apache.org/downloads.html (Can be installed without hadoop)
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 ====== Edizioni Precedenti ====== ====== Edizioni Precedenti ======
 ^ Pagina dedicata ^ Anno accademico di riferimento ^ ^ Pagina dedicata ^ Anno accademico di riferimento ^
-| [[mds:ddam:ddam20192020|Distributed Data Analysis and Mining]] | 2019-2020 |+| [[mds:ddam:ddam20202021|Distributed Data Analysis and Mining]] | 2020-2021 |
mds/ddam/start.1604447503.txt.gz · Ultima modifica: 03/11/2020 alle 23:51 (19 mesi fa) da Roberto Trasarti