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# Artificial Neural Networks

## Artificial Neural Networks

Sat. and Mon. from 08:00 am to 09:30 am

Room #3, Abourayhan Building

2^{nd} Semester 1397-1398 (Jan. 2019)

**Instructor**: Dr. Mohammad Reza Ahmadzadeh

**Room Location:** ECE Building - 322

**Internal Telephone:** Ext. 5370

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

**Fax: **Iran: +98 (0)31 33912451

**Email:**

Ahmadzadeh @ iut.ac.ir

Ahmadzadeh.m @ gmail.com

**Office Hours:**

I will try to be in my office on Saturdays, Mondays and Wednesdays from 9 :30 to 12 :00 am and 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 Artificial Neural Networks.

**Outline:**

•Introduction

–Linear algebra review

–Probability and calculus chain rules

–Introduction to machine learning

•Multilayer Perceptrons

•Logistic regression and MLP

•Backpropagation

•Stochastic gradient descent

•Optimization

Generalization

•Radial-basis function networks

Convolutional Networks

•Recurrent Neural Nets

**Textbook:**

•Charu C. Aggarwal, Neural Networks and Deep Learning. A Textbook-(2018)

•Martin T. Hagan, Howard B. Demuth, Mark Beale, Orlando De Jesús, Neural Network Design. 2014.

•Simon Haykin, Neural Networks and Learning Machines, 3rd Edition, 2009.

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

•Christopher M. Bishop, Neural Networks for Pattern Recognition, 1995.

•Michael Nielsen, Neural Networks and Deep Learning, 2017 (Online).

•Ke-Lin Du, M. N. S. Swamy, Neural Networks and Statistical Learning, Springer London 2014.

**Optional**:

**Lecture Notes:**

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

Link: http://lms.iut.ac.ir/

Prerequisites:

Linear algebra: basic matrix operations, span, rank, range and null space, eigenvalue decomposition, singular value decomposition, pseudoinverse

Probability: independence, conditional probability, Bayes rule, multivariate Gaussian distribution, marginalization, expectation, variance

Grading Policy:

Midterm Exam ≈ 25%

HW, Comp. Assignments and projects: ≈ 30%

Final exam ≈ 45%

Prerequisites:

Linear algebra: basic matrix operations, span, rank, range and null space, eigenvalue decomposition, singular value decomposition, pseudoinverse

Probability: independence, conditional probability, Bayes rule, multivariate Gaussian distribution, marginalization, expectation, variance

Grading Policy:

Midterm Exam ≈ 25%

HW, Comp. Assignments and projects: ≈ 30%

Final exam ≈ 45%

Time:

8:00 - 9:30 Sat. Mon.

Term:

2019

Grade:

Graduate

Linear algebra: basic matrix operations, span, rank, range and null space, eigenvalue decomposition, singular value decomposition, pseudoinverse

Probability: independence, conditional probability, Bayes rule, multivariate Gaussian distribution, marginalization, expectation, variance

Midterm Exam ≈ 25%

HW, Comp. Assignments and projects: ≈ 30%

Final exam ≈ 45%

Sat. and Mon. from 08:00 am to 09:30 am