Course: Optimization For Data Science

Lecturer: Bijan Ahmadi Kakavandi
The Webpage of the Course: Optimization for Data Science
Data Science Center, Shahid Beheshti University

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Index:

• Course Overview
• Main TextBooks
• Slides and Papers
• Additional Concepts and Projects
• Class Time and Location
• Grading
• Prerequisites
  • Linear Algebra
• Questions

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Course Overview:

The course will cover optimization techniques used especially for machine learning and data science. 
Because these fields typically give rise to very large instances, first-order optimization 
(gradient-based) methods are typically preferred.

Main TextBooks:

• Convex Optimization by S. Boyd and L. Vandenberghe
• Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
  

Slides and Papers:

Useful Slides by Pradeep Ravikumar and Aarti Singh

Additional Concepts and Projects:

Some additional concepts about hyperparameters optimization for machine learning and student projects related to these topics (Spring 2022)

Class Time and Location:

Saturday and Monday 13:30-15:00 AM (Spring 2019), Room 204/1.

Grading:

• Midterm – 40%
• Endterm – 60%

Final Exam:

Final Examination: Saturday 1398/04/02, 08:30-10:30

Prerequisites:

General mathematical sophistication and Linear Algebra at the advanced undergraduate or beginning graduate level, or equivalent.

Linear Algebra:

• Video: Professor Gilbert Strang's Video Lectures on linear algebra.

Questions?

I will be having office hours for this course on Sunday (09:30 AM--12:00 AM). If this is not convenient, email me at b_ahmadi@sbu.ac.ir or talk to me after class.