ECE 225A: Probability & Statistics for Data Science

Instructor: Prof. Alon Orlitsky

The course covered a wide range of concepts in Probability and Statistics. It not only introduced me to new ideas but also highlighted the power of probabilistic reasoning and theoretical understanding. There are many concepts that I wasn’t aware of before. To highlight a few:

• The concept of Memorylessness in Geometric and Exponential distributions and its applications.
• The Right CDF approach to calculate expectations of a distribution.
• Normal distribution approximates the Binomial Distribution.
• WLLN (Weak Law of Large Numbers) is a great generalisation of the CLT.
• How weak bounds like Markov can be used to derive strong bounds like the Chernoff bounds.

What I liked the most about the course was that the concepts were coupled with real-world applications. For example, we discussed the question "why we should vote?" using probability, along with analogies that helped understand the concepts better, such as understanding the reciprocity of proximity using Newton's first law. What made the class even more special was Prof. Orlitsky using memes and cartoons to explain varied concepts in an engaging and interactive manner. Prof. Orlitsky really puts in a great effort in his slides and presentation.

Overall, it was a great course to pursue as a Data Science major. The concepts and mathematics really help when reading research papers in ML/DS.

Fall 2024