Skip to main content
MyCourses MyCourses
  • Schools
    School of Arts, Design, and Architecture (ARTS) School of Business (BIZ) School of Chemical Engineering (CHEM) –sGuides for students (CHEM) – Instructions for report writing (CHEM) School of Electrical Engineering (ELEC) School of Engineering (ENG) School of Science (SCI) Language Centre Open University Library Aalto university pedagogical training program UNI (exams) Sandbox
  • Service Links
    MyCourses - MyCourses instructions for Teachers - MyCourses instructions for Students - Teacher book your online session with a specialist - Digital tools for teaching - Personal data protection instructions for teachers - Workspace for thesis supervision Sisu Student guide Courses.aalto.fi Library Services - Resourcesguides - Imagoa / Open science and images IT Services Campus maps - Search spaces and see opening hours Restaurants in Otaniemi ASU Aalto Student Union Aalto Marketplace
  • ALLWELL?
    Study Skills Guidance and support for students Starting Point of Wellbeing About AllWell? study well-being questionnaire
  •   ‎(en)‎
      ‎(en)‎   ‎(fi)‎   ‎(sv)‎
  • Toggle Search menu
  • Hi guest! (Log in)

close

Can not find the course?
try also:

  • Sisu
  • Courses.aalto.fi

CS-E407513 - Special Course in Machine Learning, Data Science and Artificial Intelligence D: Seminar on Deep Learning 2022, Lectures, 6.9.2022-29.11.2022

This course space end date is set to 29.11.2022 Search Courses: CS-E407513

  1. Home
  2. Courses
  3. School of Science
  4. department of...
  5. cs-e407513 - ...
 
Syllabus
 

General

  • General

    General

    The course is held in person. The announcements will be made in slack, please join slack by clicking this link.

    CS-E407513 - Seminar on Deep Learning (4-6 ECTS)

    Responsible Teacher: Alexander Ilin

    Teachers: Ricardo Falcon Perez, Kate Haitsiukevich, Sam Spilsbury, Nicola Dainese

    Level of the Course: Master's and PhD level

    Teaching Period: I-II

    Description: In this course, we will discuss papers on deep learning and deep reinforcement learning published in this year's ICML, ICLR and NeurIPS. Each student has to

    • present one paper (from the list pre-selected by us),
    • serve as an opponent in one presentation,
    • actively participate in the discussion in slack.

    Learning outcomes:

    • getting familiar with some of the recent papers from the deep learning literature
    • learning to review academic publications
    • learning to present scientific works

    Registration: There are 12 seminar sessions, two papers will be presented in each session. Therefore, we can accept maximum 24 students to the course. Registration for the course will be prioritized by:

    1. Study level (PhD, MSc)
    2. Early registration and selection of the topic.
    3. Grade in the Deep Learning course.

    Prerequisites: CS-E4890 - Deep Learning. This course covers advanced topics and therefore you should be comfortable with the basics.

    Highly recommended: ELEC-E8125 - Reinforcement learning. We usually discuss a few papers on RL. If you do not know the basics of RL, it will be difficult for you to discuss those papers. But active participation in the discussion of non-RL papers should be enough to pass the course.

    Grading: Grade from 0 to 5. The grade will be based on your presentation (30%), opponent speech (20%) and participation in the discussion (50%). Full points for participation can be obtained by contributing to the discussion of 50% (12) of the papers.

    Format of the seminars: The seminars are held in person. Two papers are discussed in each session with a 10-minute break between the papers.

    The format of discussion of one paper:

    • Presentation (max 15 minutes).
    • Opponent speech (max 5 minutes).
    • Discussion moderated by the teachers.

    More information can be found here.

    Discussion in slack: The discussion of the papers will happen primarily in slack. During the seminar sessions, we will only briefly summarize the slack discussion. We will also use slack for course announcements. Please join the slack workspace by following this link.

    IMPORTANT: Students are expected to discuss papers in slack before the start of seminar sessions so that the teachers could select discussion points for the seminars. Asking questions, sharing links to related resources, trying to give answers to questions, raising concerns, pointing out limitations, sharing ideas of possible extensions of the discussed work: all of that counts as participation. Please try to give a critical view on the discussed paper instead of writing a brief summary of it.
    • icon for activity
      ForumAnnouncements Forum

Course home

Course home

Next section

Seminar schedule►
Skip Upcoming events
Upcoming events
Loading There are no upcoming events
Go to calendar...
  • CS-E407513 - Special Course in Machine Learning, Data Science and Artificial Intelligence D: Seminar on Deep Learning 2022, Lectures, 6.9.2022-29.11.2022
  • Sections
  • General
  • Seminar schedule
  • Information for participants
  • Assessments
  • Home
  • Calendar
  • Learner Metrics

Aalto logo

Tuki / Support
Opiskelijoille / Students
  • MyCourses instructions for students
  • email: mycourses(at)aalto.fi
Opettajille / Teachers
  • MyCourses help
  • MyTeaching Support form
Palvelusta
  • MyCourses rekisteriseloste
  • Tietosuojailmoitus
  • Palvelukuvaus
  • Saavutettavuusseloste
About service
  • MyCourses protection of privacy
  • Privacy notice
  • Service description
  • Accessibility summary
Service
  • MyCourses registerbeskrivining
  • Dataskyddsmeddelande
  • Beskrivining av tjänsten
  • Sammanfattning av tillgängligheten

Hi guest! (Log in)
  • Schools
    • School of Arts, Design, and Architecture (ARTS)
    • School of Business (BIZ)
    • School of Chemical Engineering (CHEM)
    • –sGuides for students (CHEM)
    • – Instructions for report writing (CHEM)
    • School of Electrical Engineering (ELEC)
    • School of Engineering (ENG)
    • School of Science (SCI)
    • Language Centre
    • Open University
    • Library
    • Aalto university pedagogical training program
    • UNI (exams)
    • Sandbox
  • Service Links
    • MyCourses
    • - MyCourses instructions for Teachers
    • - MyCourses instructions for Students
    • - Teacher book your online session with a specialist
    • - Digital tools for teaching
    • - Personal data protection instructions for teachers
    • - Workspace for thesis supervision
    • Sisu
    • Student guide
    • Courses.aalto.fi
    • Library Services
    • - Resourcesguides
    • - Imagoa / Open science and images
    • IT Services
    • Campus maps
    • - Search spaces and see opening hours
    • Restaurants in Otaniemi
    • ASU Aalto Student Union
    • Aalto Marketplace
  • ALLWELL?
    • Study Skills
    • Guidance and support for students
    • Starting Point of Wellbeing
    • About AllWell? study well-being questionnaire
  •   ‎(en)‎
    •   ‎(en)‎
    •   ‎(fi)‎
    •   ‎(sv)‎