CS-E577005 - Special Course in Complex Systems D: Computational Theories of the Brain, Lectures, 25.4.2024-6.6.2024

Pääsy vierailijana
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

Credits: 5

Schedule: 25.04.2024 - 06.06.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Stephane Deny

Contact information for the course (applies in this implementation):
Professor:
Stephane Deny: stephane.deny@aalto.fi

Teaching Assistants:
Raymond Khazoum: raymond.khazoum@aalto.fi
Andrea Perin: andrea.perin@aalto.fi
Shamsi Abdurakhmanova: shamsiiat.abdurakhmanova@aalto.fi

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

  • applies in this implementation

    CS-E577005 Special Course in Complex Systems D: Computational Theories of the Brain, Period V (25.4.–6.6.2024) (5 ECTS)

    At the origin of human thought, the brain and mind have fascinated researchers from all disciplines, including philosophy, psychology, computer science, mathematics, physics and neuroscience. In this course, you will learn about some of the prominent computational theories of the brain. You will study in depth, alone or in groups (depending on the number of participants), one of these theories by reading the primary source scientific articles collected by the professor. You will then present this theory to the class in an in-depth exposé. The subject being interdisciplinary in nature, students from all quantitative backgrounds are welcome to apply, including majors in Mathematics, Physics, Computer Science, Engineering, Computational Neuroscience. Prior knowledge of dynamical systems, linear algebra, and/or machine learning is recommended. The course is capped to 18 students, and students will be prioritised based on their major and motivation (one paragraph of motivation is required to register to the class). Level recommended: Master or Doctoral Studies. Language of teaching: English.

    Dates:

    Thu 25.04.2024 14:15 - 16:00, R001/M205
    Thu 02.05.2024 14:15 - 16:00, R001/M205
    (9.5. Ascension Day - no teaching)
    Thu 16.05.2024 14:15 - 16:00, R001/M205
    Thu 23.05.2024 14:15 - 16:00, R001/M205
    Thu 30.05.2024 14:15 - 16:00, R001/M205
    Thu 06.06.2024 14:15 - 16:00, R001/M205 (on exam week)

DETAILS

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Students will be prioritised by courses they have taken. Doctoral students in relevant fields are also prioritized.
    Highly recommended:
    MS-E2112 Multivariate Statistical Analysis (or equivalent)
    CS-E5755 Nonlinear Dynamics and Chaos (or equivalent)
    Recommended:
    CS-E4710 Machine Learning, Supervised methods
    or CS-E4890 Deep Learning
    or CS-E5710 Bayesian Data Analysis
    or NBE-E4070 Basics of Biomedical Data Analysis

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