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)

Spring 19 Doktorand for single courses students

  • Periods

    Spring 19 P4 (7.5 credits)

  • Application code

    20120

  • 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

    Hossein Azizpour <azizpour@kth.se>

    Josephine Sullivan <sullivan@kth.se>

  • Target group

    For PhD-students only

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.