Mathematical Foundations of Data Science

Course: Mathematical Foundations of Data Science
Data Science Center
Shahid Beheshti University

Main TextBooks:

• Data Mining and Analysis: Fundamental Concepts and Algorithms by Mohammed J. Zaki and Wagner Meira Jr.
  Reading: Chapters 1, 2, 3, 5, 6, 7, and 21

• Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David
  Reading: Chapter 23

Slides & Papers:

Recommended Slides & Papers:

1. Chapter 1 (Data Mining and Analysis) of Data Mining & Analysis: Fundamental Concepts & Algorithms
   Slides (Data Mining and Analysis): PDF, PPT by Mohammed J. Zaki and Wagner Meira Jr.
   
2. Chapter 2 (Numeric Attributes) of Data Mining & Analysis: Fundamental Concepts & Algorithms
   Slides (Numeric Attributes): PDF, PPT by Mohammed J. Zaki and Wagner Meira Jr.
   
3. Chapter 3 (Categorical Attributes) of Data Mining & Analysis: Fundamental Concepts & Algorithms
   Slides (Categorical Attributes): PDF, PPT by Mohammed J. Zaki and Wagner Meira Jr.
   
4. Chapter 5 (Kernel Methods) of Data Mining & Analysis: Fundamental Concepts & Algorithms
   Slides (Kernel Methods): PDF, PPT by Mohammed J. Zaki and Wagner Meira Jr.
   
5. Chapter 6 (High-dimensional Data) of Data Mining & Analysis: Fundamental Concepts & Algorithms
   Slides (High-dimensional Data): PDF, PPT by Mohammed J. Zaki and Wagner Meira Jr.
   
6. Chapter 7 (Dimensionality Reduction) of Data Mining & Analysis: Fundamental Concepts & Algorithms
   Slides (Dimensionality Reduction): PDF, PPT by Mohammed J. Zaki and Wagner Meira Jr.
   
7. Chapter 21 (Support Vector Machines) of Data Mining & Analysis: Fundamental Concepts & Algorithms
   Slides (Support Vector Machines): PDF, PPT by Mohammed J. Zaki and Wagner Meira Jr.
   

Class time, Location and Examinations:

Saturday and Monday 10:00-11:30 AM (Fall 2018), Room 208.

Two written exams:

Midterm Examination: Monday 1397/08/28, 10:00-12:00
Final Examination:

Grading:

• Homework – 15%
  — Will consist of mathematical problems and/or programming assignments.
• Midterm – 35%
• Endterm – 50%

Prerequisites:

General mathematical sophistication; and a solid understanding of 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’ll follow the standard of Department of Mathematical Sciences approach:

• You can get help, but you MUST acknowledge the help on the work you hand in
• Failure to acknowledge your sources is a violation of the Honor 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
• 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

Questions?

I'll be having office hours for this course on Saturday and Monday (13:00 AM--14:00 AM). If this isn't convenient, email me at b_ahmadi@sbu.ac.ir or talk to me after class.