In C:

This assignment is designed to give you more experience programming in C and using the Unix environment. Your task will be to write one program that implements a simple machine-learning algorithm. This will require file 1/O, dynamic memory allocation, and correctly implementing an moderately complex algorithm. Machine learning (ML) techniques are increasingly used to provide services, such as face recog- nition in photographs, spelling correction, automated translation, and predicting what YouTube videos you might want to watch next. Implementing a full ML algorithm is beyond the scope of this course, so you will implement a "one shot" learning algorithm that uses historical data to predict house prices based on particular attributes. For example, a house might have zi bedrooms, r2 bathrooms, rs square footage, and be built in year r4. If we had appropriate weights, we could estimate the price of the house y with the formula The goal of one-shot learning is to find values for the weights wi using a large provided set of training data. Once those weights have been found, they can be used to estimate prices for additional houses. For example, if the training data includes n houses and has k attributes, this data can be represented as an n (k+ 1) matrix X, of the form 1 1,11,2 T1,k 1 n-1,1 n-1,2 n-1,k where each row corresponds to a house and cach column corresponds to an attribute. Note that the first column contains 1 for all rows: this corresponds to the weight wo Similarly, house prices can be represented as an n x 1 matrix Y, of the form y1 Jn-1 where each row gives the price of a house. This assignment is designed to give you more experience programming in C and using the Unix environment. Your task will be to write one program that implements a simple machine-learning algorithm. This will require file 1/O, dynamic memory allocation, and correctly implementing an moderately complex algorithm. Machine learning (ML) techniques are increasingly used to provide services, such as face recog- nition in photographs, spelling correction, automated translation, and predicting what YouTube videos you might want to watch next. Implementing a full ML algorithm is beyond the scope of this course, so you will implement a "one shot" learning algorithm that uses historical data to predict house prices based on particular attributes. For example, a house might have zi bedrooms, r2 bathrooms, rs square footage, and be built in year r4. If we had appropriate weights, we could estimate the price of the house y with the formula The goal of one-shot learning is to find values for the weights wi using a large provided set of training data. Once those weights have been found, they can be used to estimate prices for additional houses. For example, if the training data includes n houses and has k attributes, this data can be represented as an n (k+ 1) matrix X, of the form 1 1,11,2 T1,k 1 n-1,1 n-1,2 n-1,k where each row corresponds to a house and cach column corresponds to an attribute. Note that the first column contains 1 for all rows: this corresponds to the weight wo Similarly, house prices can be represented as an n x 1 matrix Y, of the form y1 Jn-1 where each row gives the price of a house