About the course

This website is devoted for two simultaneously courses Algorithms for Data Science(ADS), and Mathematical Foundations of Data Science(MFDS), which provide a comprehensive, in-depth overview of data mining, machine learning, and statistics, offering solid guidance for students, researchers, and practitioners. The website lays the foundations of data analysis, pattern mining, clustering, classification, and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts.

Class time and Location

• ADS: Saturday and Monday 08:00-09:30 AM (Fall 2023), Room 2.
• MFDS: Saturday 10:00-11:30 AM and Monday 04:00-05:30 PM (Fall 2023), Room 2.

References

• Mohammed J. Zaki and Wagner Meira Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, 2020, Cambridge University Press.
• Larry Wassennan, All Of Statistics: A Concise Course In Statistical Inference, 2003, Michigan Publishing.
• Stanley H. Chan, Introduction to Probability for Data Science, 2021.
• Dr. Hajiabolhassan’s Algorithms For Data Science Course.

The references mentioned above, along with other books required for the course, can be accessed from the FUM local folder.

Syllabuses

Grading:

• Homework – 40%
  
• Quizes – 10%
  — Will consist of mathematical problems and/or programming assignments.
• Endterm – 50%

Four Written Exams:

TBA

Prerequisites:

General mathematical sophistication; and a solid understanding of Algorithms, Linear Algebra, and Probability Theory, at the advanced undergraduate or beginning graduate level, or equivalent.

Account:

It is necessary to have a GitHub account to share your projects. It offers plans for both private repositories and free accounts. Github is like the hammer in your toolbox, therefore, you need to have it!

Academic Honor Code:

Honesty and integrity are vital elements of the academic works. All your submitted assignments must be entirely your own (or your own group’s).

We will follow the standard of Faculty of Mathematical Sciences approach:

• You should not use code of others or be looking at code of others when you write your own: You can talk to people but have to write your own solution/code
• You can talk to others about the algorithm(s) to be used to solve a homework problem; as long as you then mention their name(s) on the work you submit

Questions?

I will be having office hours for this course on Monday (10:00 AM–11:30 AM). If this is not convenient, email me at m.amintoosi@um.ac.ir or talk to me after class.

Our Slack workspace

Our computer science group has a Slack workspace where we can chat and share ideas. To join us, click this link and follow the instructions.