MachineLearninginAction机器学习实战及配套代码

MachineLearninginAction机器学习实战及配套代码

After college I went to work for Intel in California and mainland China. Originally my plan was to go back to grad school after two years, but time flies when you are having fun, and two years turned into six. I realized I had to go back at that point, and I didn’t want to do night school or online learning, I wanted to sit on campus and soak up everything a university has to offer. The best part of college is not the classes you take or research you do, but the peripheral things: meeting people, going to seminars, joining organizations, dropping in on classes, and learning what you don’t know. Sometime in 2008 I was helping set up for a career fair. I began to talk to someone from a large financial institution and they wanted me to interview for a position modeling credit risk (figuring out if someone is going to pay off their loans or not). They asked me how much stochastic calculus I knew. At the time, I wasn’t sure I knew what the word stochastic meant. They were hiring for a geographic location my body couldn’t tolerate, so I decided not to pursue it any further. But this stochastic stuff interested me, so I went to the course catalog and looked for any class being offered with the word “stochastic” in its title. The class I found was “Discrete-time Stochastic Systems.” I started attending the class without registering, doing the homework and taking tests. Eventually I was noticed by the professor and she was kind enough to let me continue, for which I am very grateful. This class was the first time I saw probability