Course Description
The course helps to understand the fundamentals of Deep Learning. The course starts off gradually with multilayer preceptrons and it progresses into the more complicated concepts such as attention and sequencetosequence models. This course also covers other models of deep learning such as convolutional neural networks, recurrent neural networks, deep generative models such as autoregressive, GAN, VAE, NFM, representation learning, and deep reinforcement learning methods. We use frameworks such as PyTorch and Tensorflow, which are very important for implementing deep Learning models.
Course Information
Instructor: Hamid Beigy
Teaching Assistants: Fariba Lotfi, Fatemeh Karimkhani, Aryan Sadeghi, Fatemeh Sarshar Tehrani, Amirhossein Noohian, Amirhossein Youssefi, Amirhossein Sohrabbeig, Fatemeh Sarhsar Tehrani, Maryam Afshari,
Maryam Riyahi Madvar, Mostafa Norouzi, Paria Kashani, Yasaman Boreshban
Time and Location: Saturday/Monday: 10:30 am 12:00 am, virtual class
Contact: Please contact the teaching staffs on Piazza site.
Required Texts
[GYC] Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.
[DY] Li Deng and Dong Yu, Foundations and Trends in Signal Processing, Vol. 7, No. 3–4, pp 197387, 2014.
[SB] Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, Second edition. The MIT Press, 2018.
Grading Policy
15%: Midterm exam 1 (1400/01/19).
15%: Midterm exam 2 (1400/02/22).
20%: Final exam (1400/04/07).
30%: Homeworks.
15%: Quiz.
5%: Paper & Explore a theoretical or empirical question and present it. Deadline for choosing paper: 1400/02/25.
Lecture Schedule
Problem sets
Problem set 1 Due 14000106
Problem set 2 Due 14000127
Problem set 3 Due 14000218
Problem set 4 Due 14000318
Problem set 5 Due 14000425
