Syllabus
Instructors
Instructor: Qiang Sun and Eric Moulines
Lectures: Tuseday, 12:00 - 1:20pm, CR3; Thursday, 11:00 - 12:20, CR1
Office Hours: Thursday 12:30 - 13:30, B-3.05
Email: qiang.sun@mbzuai.ac.ae and eric.moulines@mbzuai.ac.ae
TA: Ding Bai, ding.bai@mbzuai.ac.ac
Lab: Friday, 9:00 - 10:50, CR1
Please email if standard office hours times do not work for you.
Instructional Methods
In Person. Some classes may be scheduled on Zoom in unforeseen circumstances.
Course Description
A central goal in statistics is to use data to build models that allow us to make inferences about the underlying data-generating processes or predict future observations. Although real-world problems are often complex, the linear model frequently provides a good approximation to the true data-generating process. Moreover, linear models possess elegant algebraic and geometric properties and often admit explicit formulas, offering deep insights into various aspects of modern machine learning. In our experience, the insights gained from linear models are broadly applicable, with only rare exceptions.
Course Materials
All materials are available as publicly available research papers linked from the course schedule.
Textbook
- Lecture Notes
- Ding 2025, Linear Model and Extensions
- Dobson and Barnett 2018, An Introduction to Generalized Linear Models
Reference Books
- Agresti 2015, Foundations of Linear and Generalized Linear Models
- Knight 2000, Mathematical Statistics
- Hogg, McKean, Craig 2000, Introduction to Mathematical Statistics
- Evans and Rothenthal 2024, Probability and Statistics: The Science of Uncertainty
Prerequisites and Corequisites
This course assumes basic training in linear algebra, probability theory, and statistical inference.
Course Assignments and Requirements
- Lecture Particpation: 10%
- Lab Particpation: 10%
- Assignments: 20%
- midterm Exam: 20%
- Final Exam: 40%
Lecture and Lab Participation
To earn participation points, participate in discussions and ask questions during lectures and online on Moodle Discussions.
Student Survey of Teaching
You will have multiple opportunities to provide feedback on your experience in this course. Suggestions and constructive criticism are encouraged throughout the course and may be particularly valuable early in the semester. To that end, we may use surveys and/or reflection assignments to gather input on what is working well and what could be improved. You will also be asked to complete an end-of-semester, online Student Survey of Teaching, which could inform modifications to this course (and other courses that I teach) in the future.
Attendance Policy
All course materials will be shared online after class. You are expected and encouraged to attend and participate in class. There are 20% points for lecture and lab participation. If you have any problems which will cause you to miss a class/lab or an assignment deadline please email me.
The [University Excused Absence Policy] is a good reference for allowed reasons.
Subject to Change Statement
The schedule for the class beyond next week is subject to change. Please consult the course website to know the latest version of the schedule and syllabus. Any changes in assignments and syllabus policies will conveyed via Canvas announcements.
Student Resources and University Policies
Please visit the Student Resources website for a list of student resources and university policies.
Academic Integrity
Academic misconduct of any kind will automatically result in a 0 score on the assignment and your actions will be reported to the university. You can discuss with peers, however your work must be your own. Posting your assignments to internet discussion lists / forums / chatrooms is considered academic misconduct. Directly sharing your solutions with others is also considered academic misconduct.