INF554 - Machine and Deep learning (2022-2023)

Edition 2020-21

We have entered the Big Data Era. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines – ranging from science and engineering to business and society in general. A major challenge is how to take advantage of the unprecedented scale of data, in order to acquire further insights and knowledge for improving the quality of the offered services, and this is where Machine and Deep Learning comes in capitalizing on techniques and methodologies from data exploration (statistical profiling, visualization) aiming at identifying patterns, correlations, groupings, modeling and doing predictions. In the last years Deep learning is becoming a very important element for solving large scale prediction problems. 

The Introduction to Machine and Deep Learning class will cover the following aspects:

  • The Machine Learning Pipeline
  • Unsupervised Learning
  • Data Preprocessing and Exploration
  • Feature Selection/Engineering & Dimensionality reduction
  • Supervised Learning 
  • Deep and Reinforcement Learning

 

Logistics

1. The course will take place on Mondays afternoon from 21/09/2020 for 9 weeks and will be divided into nine 4-hour sessions Due to the COVID situation we will conduct the course/labs with VIDEO online synchronous classes/labs: 

- magistral teaching (14:00 - 16:00) FOLLOW THIS ZOOM LINK FOR ALL CLASSES

-  online lab sessions (16:15 - 18:15) FOLLOW THIS ZOOM LINK FOR ALL LABS

 

Interaction/Q&As: 
As there is currently lack of physical co presence a slack channel was set up for individual questions to course/lab teachers: https://inf554workspace.slack.com. Please us it as much as possible. 
We will also set up some online video time slots to handle your questions in real time. 
 
We will also try to provide recorded versions of the class and the labs if the storage requirements can be met. 

IPPMaster and other students - access: 

you may get access to the course and material by registering to this form: 

we will then send you your guest access code. 

2. The students will have to complete the course work on their own laptops (preferably with a Unix environment like Linux or Mac OS X for compatibility reasons). As for software, we will be using  Python among others (to be installed locally on the laptops). Students are invited to install the Anaconda distribution version 3.7 BEFORE the 1st lab session.