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Deep Learning

Deep Learning

2nd Semester 401-402 (Jan. 2023)


Class Meetings: Sunday and Tuesday from 9:30am to 11:00am

Room #29-ECE Build.

Instructor: Dr. Mohammad Reza Ahmadzadeh

Office Location: ECE Building - 212B

Internal Telephone: Ext. 5370

External Telephone: Iran: +98 (0)31 33915370

Fax: Iran: +98 (0)31 33912451

Email: Ahmadzadeh @



Office Hours:

I will try to be in my office on Sundays and  Tuesdays from 11:00to 12:00 am, but I will always do my best to be available for students by appointment or any time that I am free.

Course Description:

This course covers the fundamentals of Deep Learning techniques.


Linear algebra review
Probability and calculus chain rules
Introduction to machine learning 
Multilayer Perceptrons
Logistic regression and MLP
Stochastic gradient descent
Convolutional Networks 
Image Classification
Recurrent Neural Nets 
Exploding and Vanishing Gradients
ResNets and Attention
Learning Probabilistic Models
Mixture Modeling 
Boltzmann Machines
Bayesian Hyperparameter Optimization
Adversarial Learning
Deep Reinforcement Learning
Embedding learning


I. Goodfellow, Y. Bengio, A. Courville, Deep Learning 2016.


Aurelien Geron, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2019.
François Chollet, Deep Learning with Python, 2021.
Andrew W. Trask, Grokking Deep Learning, 2019.
Daniel A. Roberts and Sho Yaida, Deep Learning Theory, 2021.
C. M. Bishop, Neural Networks for Pattern Recognition, 1995.
Michael Nielsen, Neural Networks and Deep Learning, 2017 (Online).
K. L. Du, M. N. Swamy, Neural Networks and Statistical Learning, 2014.
S. Haykin, Neural Networks and Learning Machines, 3rd Ed., 2009.


Lecture Notes:

video Lectures (2023)

Slides modified by Instructor and lecture notes can be obtained via E-Learning LMS (Enrolled students, Password protected).



Probability and Stochastic Processes for Engineers or equivalent.

Grading Policy: 

Midterm Exam ≈25%±5%

HW, Comp. Assignments and projects: ≈ 30%

Final exam ≈ 45%±5%



Sun. and Tue. from 09:30am to 11:00am


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