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Computational Intelligence-Fall 2019

Course Name 



Computer Science


3 units


Fall 2019

Lecture Time

Saturday and Monday, 12:00- 14:00


Amir Salarpour — e-mail:

Teacher Assistant

Pedram MohajerAnsari — (Tuesday 14:00 – 16:00)




Text Book(s)              

Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.

Course Objectives

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress. So, in this course will explain Machine Learning modules in details.

Course contents

        • Introduction to Machine Learning
        • Matlab Tutorial
        • Linear Algebra Review
        • Linear Regression with One Variable
        • Logistic Regression
        • Linear Regression with Multiple Variables
        • Regularization
        • Neural Networks: Representation
        • Neural Networks: Learning
        • Support Vector Machines
        • Advice for Applying Machine Learning
        • Unsupervised Learning
        • Dimensionality Reduction
        • Anomaly Detection

This course is designed to provide fundamental prerequisites for students who are enthusiastic about pursuing Machine Learning course. So, if you want to break into AI, Deep Learning course will be the second step in getting insight into Machine Learning Theory. Deep Learning is one of the most highly sought-after skills in tech.