Rigorous treatment of various topics in Data Science. Majority of class is 3rd/4th year PhDs, with very few masters students and visiting professors.
Although programming languages and compilers are still important and highly skilled individuals are needed in these area, the vast majority of researchers will be involved with applications and what computers can be used for, not how to make computers useful. With this in mind we have written this book to cover the theory likely to be useful in the next 40 years just as automata theory and related topics gave students an advantage in the last 40 years. One of the major changes is the switch from discrete mathematics to more of an emphasis on probability and statistics.
– John Hopcroft
graduate level course and requires a sophisticated mathematical maturity along with probability
– John Hopcroft
Knowledge of physics/electrical engineering will also be useful in some parts of the course.
High Dimensional Space, Randomized Algorithms, Statistical Learning Theory, Random Graphs, Compressed Sensing
3 problem sets weekly. However, problems are much different than the undergraduate version (CS 4850). Most problems are proofs or open research problems, some of which you are told are not expected to be solved (or can be) but just attempt. Homework can easily take upwards 20-25 hours a week, or even more depending on the difficulty.
Discuss problems with professors and other students in the class, and start the day problems are assigned.
Fall 2013 Most intellectually stimulating course I’ve taken at Cornell and has helped me with research and future career paths. I spent many hours each week discussing the problems/research areas with Prof. Hopcroft- he encourages any student to come in and talk. Out of the 200 credits I’ll graduate with, these were the only 4 where I felt challenged on a daily basis.
|Semester||Time||Professor||Median Grade||Course Page|
|Fall 2013||9:05 - 9:55||John Hopcroft||N/A||N/A|