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DD2424 Deep Learning in Data Science 7.5 credits

Djupinlärning i Data Science

  • Education cycle

    Second cycle

  • Main field of study

    Computer Science and Engineering

  • Grading scale

    A, B, C, D, E, FX, F

Course offerings

Spring 19 deep19 for programme students

Spring 19 deep19 for Study Abroad Programme (SAP)

  • Periods

    Spring 19 P4 (7.5 credits)

  • Application code

    20072

  • Start date

    18/03/2019

  • End date

    04/06/2019

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

  • Course responsible

    Josephine Sullivan <sullivan@kth.se>

  • Teacher

    Josephine Sullivan <sullivan@kth.se>

  • Target group

    Only open för students within Study Abroad Programme (SAP)

  • Application

    Apply for this course at antagning.se through this application link.
    Please note that you need to log in at antagning.se to finalize your application.

Spring 20 deep20 for programme students

Autumn 18 deepl18 for programme students CANCELLED

  • Periods

    Autumn 18 P1 (7.5 credits)

  • Application code

    51545

  • Start date

    27/08/2018

  • End date

    26/10/2018

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

Intended learning outcomes

After the course, you should be able to:

  • explain the basic the ideas behind learning, representation and recognition of raw data
  • account for the theoretical background for the methods for deep learning that are most common in practical contexts
  • identify the practical applications in different fields of data science where methods for deep learning can be efficient (with special focus on computer vision and language technology)

in order to:

  • be able to solve problems connected to data representation and recognition
  • be able to implement, analyse and evaluate simple systems for deep learning for automatic analysis of image and text data
  • receive a broad knowledge enabling you to learn more about the area and read literature in the area

Course main content

  • Learning of representations from raw data: images and text
  • Principles of supervised learning
  • Elements for different methods for deep learning: convolutional networks and recurrent networks
  • Theoretical knowledge of and practical experience of training networks for deep learning including optimisation using stochastic gradient descent
  • New progress in methods for deep learning
  • Analysis of models and representations
  • Transferred learning with representations for deep learning
  • Application examples of deep learning for learning of representations and recognition

Eligibility

Non-programme students: 90 university credit points including 45 credits in mathematics or informatics

Literature

Ian Goodfellow, Aaron Courville, and Yoshua Bengio "Deep Learning"

Material produced at the department

Examination

  • LAB1 - Laboratory work, 4.5, grading scale: A, B, C, D, E, FX, F
  • TEN1 - Examination, 3.0, grading scale: A, B, C, D, E, FX, F

By making an optional project assignment the students can improve their final grade.

Offered by

EECS/Intelligent Systems

Contact

Josephine Sullivan, e-post: sullivan@kth.se

Examiner

Josephine Sullivan <sullivan@kth.se>

Version

Course syllabus valid from: Spring 2019.
Examination information valid from: Spring 2019.