Machine Learning

    Deep learning is the study of computer algorithms improving automatically through experiences. It is considered as a subset of Artificial Intelligence (AI).

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    Deel Learning

    Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks

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    University of Tehran

    The University of Tehran is the oldest modern university located in Tehran, Iran. It is also one of the most prestigious universities in the Middle East

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    Course Description


    Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks we focus on six topics of deep learning: 1- Layers, Blocks and Modules 2- Complexity Indices and Data analysis 3- Architectures 4- Layer Wise Analysis Algorithms 5-Layer Wise Design Algorithms 6- Deep Metric Learning

    Team


    Babak Nadjar Araabi

    Instructor

    Ahmad Kalhor

    Instructor

    Ali Karimi

    Chief Teacher Assistant

    Melika Sadeghi

    Teacher Assistant

    Erfan Rasouli

    Teacher Assistant

    Soroosh Dehdashtian

    Teacher Assistant

    Ali Sabzejou

    Teacher Assistant

    Ali Rashidi Moghadam

    Teacher Assistant

    Policy


    Class Time and Location

    Fall term (Sep 2023 - Feb 2024).
    Lecture: Saturday, Monday 10:30am-12am (Class:220)

    Office Hours

    You can find a full list of times and locations on the calendar.

    Grading Policy (flexible)

    Homeworks: ~75%
    Final exam: ~25%

    Course Discussions

    UT Students: Elearn

    Telegram Channel

    Assignment Details

    See the Homework Page for more details on how to hand in your assignments.

    Prerequisites


    • Proficiency in Python:
      All class assignments will be in Python. If you have a lot of programming experience but in a different language (e.g., C/C++/Matlab), you will probably be fine.
    • Neural Networks:
      You should know the basics of neural networks, deep learning, etc.
    • Backpropagation:
      We will be formulating cost functions, taking derivatives, and performing optimization with gradient descent.