Lesson Description

In this course, you will learn fundamental and practical topics in Machine Learning (ML), specifically advanced topics on convolutional neural network (CNN)s, understand how to build advanced ML & neural networks, and learn how to lead successful machine learning projects. This course provides a broad introduction to machine learning pattern recognition. Topics include Supervised Learning; Unsupervised Learning; Learning Theory; Feature Engineering; and Model Evaluation and Improvement.

Weekly Schedule

Mondays 8-10, Location: Learning Management System (LMS)

 

Files

Files Name Presentations Home works Quizzes Midterm Final Exam Related Documents
Chapter 1 Download Download Download Download Download Download
Chapter 2 Download Download Download Download Download Download
Chapter 3 Download Download Download Download Download Download
Chapter 4 Download Download Download Download Download Download
Chapter 5 Download Download Download Download Download Download
Chapter 5 Download Download Download Download Download Download
Chapter 6 Download Download Download Download Download Download
Chapter 7 Download Download Download Download Download Download
Chapter 8 Download Download Download Download Download Download

Course References



1. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, "Foundations of Machine Learning", MIT Press, Second Edition, 2018.

2. Bishop, Christopher M. "Pattern Recognition and Machine Learning", 2006.

3. Kevin P. Murphy, "Machine Learning: A Probabilistic Perspective", MIT press, 2012.

4. Ian Goodfellow, Yoshua Bengio, Aaron Courville, "Deep Learning", An MIT Press book, online: https://www.deeplearningbook.org/ .

Current Teacher Assistants

Responsibilities: Teacher in Problem-Solving classes, Solving Homework Assignments & Quizez, Teaching Python

Sepideh Rashidianfar

TA

Former Teacher Assistans

-

 

Contact

Location:

HerzarJarib  Street, Azadi Square, University of Isfahan, Isfahan, Iran

Call:

+98 31 3793 5638

Loading
Your message has been sent. Thank you!