Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

Understanding of the general principles of deep learning, and the central deep learning methods discussed in the course. After the course, you should be able to apply them to real-world data sets.

Credits: 5

Schedule: 01.03.2022 - 27.05.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Alexander Ilin

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content
  • valid for whole curriculum period:

    Fundamental and current topics of deep learning. Implementing algorithms on a computer are a part of the course and the programming language is Python. Python-based softwares that allow for symbolic differentiation will also be used.

Assessment Methods and Criteria
  • valid for whole curriculum period:

    Exercises and exam.

Workload
  • valid for whole curriculum period:

    1 lecture per week (1h 30 min total each), one computer exercise session per week (1 h 30 min total each), and the rest for studying the course material and doing exercises.

DETAILS

Study Material
  • valid for whole curriculum period:

    Material produced for the course such as lecture slides, external material. The external material will include the 'Deep Learning'-book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, published by MIT Press in 2016 (online version freely available at http://www.deeplearningbook.org), Python material.

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Teaching Period:

    2020-2021 Spring IV-V

    2021-2022 Spring IV-V

    Course Homepage:

    https://mycourses.aalto.fi/course/search.php?search=CS-E4890

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