Mohammad Maleki

I learned multiple ideas of probability and statistics with strong foundational concepts through the course. The process of learning from the the beginning of the field's development, such as cardano's definition of probability and the deviance of the field from that definition, definitely played a huge role in my success to understand the more challenging concepts of conditional probabiltiy and random variables as it provided a logical step forward avoiding any confusions.

Advanced topics in theoretical statistics especially relevant to high dimensional problems, including concentration inequalities and random matrix theory.

A looooot of statistics! This is a heavy course, but time-commitment and hard work will certainly pay off. I love the real world examples Prof. Maleki gives/gave us during lecture and the applicability of the material.

Statistics are everywhere so it is a very important topic to discuss and understand. Especially in the beginning of the course, the examples were easy to picture and applications were clear. Later in the class it became a bit more theoretical/abstract which could make them more difficult to practically reason about.

I learned how to write the notation of probability theory, a few interesting ideas, and gained a much better understanding of how to approach probability problems through the homework assignments. I liked the difficulty of the homeworks but would prefer them to be even harder for even better practice on difficult probabilistic thinking. I learned mostly through doing the homeworks.

I learned a lot of statistical tools and applications.

Calc-based intro to probability and statistics

Necessary analysis tools in statistics and probability to solve engineer problems

As professor Maleki emphasized, this course really opened my eyes to a new way of thinking about probability. I very much enjoyed the examples he gave in class, and it was a very enjoyable class.

Basic probability: conditional probability, discrete and continuous random variables, expectation value, variance, joint distributions