This post is about self driving cars. If u follow the news u would know that there is already an effort to create auto cars or cars that drive themselves. How is statistical pattern recognition applicable here?
Regression can possibly help us here. Regression is a tool which learns a function whose output is continuous (Models in previous posts were discrete). The idea is simple. Let a human drive a car which also has a on board camera and a device to capture the drivers response to various events on the camera. Over time with a good learner and a good driver, it is possible that the learning system may learn how to drive. There can be a preprocessing stage in front of the camera to seperate out obstacles and other moving bodies. Also it may be necessary to find out the speed of a moving object and try to project it in time and provide the projection data to the learner, this may be necessary as it is impossible to encounter all the auto drivers for a trainer in the training period. (This is based on the assumption that autos are quantum vehicles whose absolute position given its current position can only be determined probabilistically).
Since a rigorous proof that this system will work cannot be given it is better to prove it by building such a system. The only problem is the funding.
some of the other stolen ideas include
- stock market prediction: People are already using machine learning techniques to predict the movement of the stock market. It is said that some of the banks make a lot of money by predicting the fluctuations in the currencies of different countries. I guess there is a lot of money that can be made by creating the write model by predicting the movemets of a financial institution.
- an other idea that one of my friends is working on is to identify related tunes from different songs, or given a tune find the most appropriate song from a database.
Welcome to the first predict guru idea, this blog is about our experiments to get to know the ideas behind machine learning. Since the time i started learning this fascinating subject, (more scary than fascinating for equation haters like me) i felt like we can apply this technique for many more things other than Fishers IRIS data or the US postal departments ocr data. The following are the things i am planning to try out.
- Cricket player score prediction: Lots of money is involved in cricket betting, imagine the amount of money u can make if u know how much Tendulkar or Sehwag is going to score in a series. I guess given a right model this can be easily predicted, I mean if not the exact number of runs, a range in which each player will score in a series of say 5 matches. Look out for ideas for the model later. This idea can be extended to other games as well.
- This idea is about predicting people. Companies spend a lot of money trying to get the right person, right now interviewing and testing is more of an art which is left to the existing employees who may not be readily available. Why not use machine learning techniques here. One way to do it is to provide a questionnaire to the interviewee as well as some of the ideal employees of an organization. Based on the distance between the expected and the interviewees response, a HR person can quickly discover if a candidate can move forward to the next level.
- Software code review is another area where people use subjective assesments. Again code reviewed by experts can be compared with the new code to evaluate it.
Although some of these look like jobs for expert systems, the idea here is to learn the rules statistically.