machine learning

Content:
On monday we had a lecture on machine learning by Mr.Ji. Since I am from non computerscience background I kept my eyes and ears open to understand the tpoic. I learned a lot of things in lecture but I was still not clear about the whole concept, so I used web and tried to relate what I learned in class. Here is what  I managed to understand.machine learning is defined as changes  in system that perform recognition,diagnosis,planning, prediction etc.so that we can get a result as desired.Its main goal is to turn data which is given as an input into output.After learning from collection of data we want machine or the system to answer questions based on that data.The machine turns data into information by extracting patterns from the data.
A machine learning data can be supervised or unsupervised. Supervised data have labels that goes with the data features, while unsupervised data donot have labels.When the data has categories as labels then we are doing clssification and when the data is numeric we call that we ar doing regression i.e. we are trying to fit a numeric output given some numeric input data.When we have a data without the labels and when we want to check to which category the data falls then we do clustering.The goal is to group unlabelled data which are 'close' to.
The lecture was mainly based on three types of classification algorithms.I thought that k nearest neighbour was simplest among three and the use of duck examples made it easy to understand. What i think that we look  for maximunm likelihood. The second one being the decision tree algorithm which is a kind of decision support tool which helps in modelling a decision and its consequence.Please correct me if I am wrong , when Dr. Shortliffe discussed about the example of a 95 yr patient who had a tumor in lungs in the decision analysis lecture , I think the decision analysis for this patient was done through a decision tree. The advantage of decision tree is that it is simple to understand and interpret and it can easily be combined with other decision techniques.The third was Support vector machine which finds boundry to seperate data types .however I have not  fully understood the concept of support vector machine but I hope that maybe in his next lecture I can have better understanding.I look forward to his next lecture.

Posted by
Ashutosh Singraur