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Lectures: Tuesdays & Thursdays 3:30-4:50PM 📍Podemos 1A18

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Course description

This graduate course introduces the theory and methods of high-dimensional probability and statistics, focusing on tools for analyzing random phenomena in large-scale data and learning problems. Core topics include concentration inequalities, matrix concentration, entropy and transportation methods, chaining, and empirical processes. We will also highlight applications in statistics and machine learning, including regression and Lasso error bounds, covariance estimation and PCA, generalization in learning theory, and random projections.

Prerequisites: Probability theory and statistics (MATH 180A/B, DSC 212), multivariate calculus (MATH 10C), and linear algebra (MATH 18). Some exposure to statistics and machine learning is useful but not required.

Course information and schedule

Time and location: Tuesdays & Thursdays 3:30-4:50PM 📍Podemos 1A18

Instructor: Tianhao Wang, [email protected]

• Office hour: schedule on-demand

TAs:

1. Zhi Wang, [email protected]
   1. Office hour: Friday 11AM-12PM, 📍HDSI 138
2. Zeyu Bian, [email protected]
   1. Office hour: Wednesday 9-10AM, 📍HDSI 155

🗓️ Schedule

Coursework

Coursework in this class will be centered on regular homework (90%) and classroom participation (10%). There will be no exams. Homework out each Thursday and due at 11:59PM on the next Friday. Homework submission and grading are on Gradescope. Both handwritten solutions and Latex write-ups are acceptable.

📄 Homework (90%)

Problem sets

🚨 Homework policy:

• Late submissions will not be accepted.
• Collaboration policy:
  • Students are encouraged to discuss general ideas and approaches with classmates.
  • However, all solutions must be written individually. Copying solutions from other students or online sources is not allowed.
  • If you discussed problems with classmates, please list their names on your submission.
• AI use policy:
  • Any use of AI tools must be accompanied by an AI-use disclosure that describes the manner of use.
  • Homework solutions or reports must be written by the student; submissions that are substantially written by AI will receive no credit.

💡 Participation (10%)