机器学习代写｜Introduction to Machine Learning Fall 2022

这是一篇来自美国的机器学习代写

Machine Learning is nowadays one of the most rapidly developing technical fields both in the academia and industry. It is also a fundamental tool used in a wide range of different data science fields. This course presents the basic concepts, techniques, and algorithms in machine learning from both theoretical and practical perspective. The program of the course includes empirical risk minimization, support vector machines, kernels, clustering, principal component analysis,Expectation-Maximization, graphical models, and neural networks.

There is no textbook required. The list of recommended texts:

 Pattern recognition and machine learning, C.M. Bishop

 Pattern classification, R. O. Duda, P. E. Hart, and D.G. Stork

 T. Jebara. Course notes, Machine Learning

 S. Dasgupta. Course notes, CSE 291: Topics in unsupervised learning

For coding, preferred environments is Matlab. Data sets for programming questions will be provided in Matlab format. However a student can choose any environment he/she likes and convert data sets to a desired format. Homeworks are due at 10.45am on the given day. Late submissions will not be approved!!!

Your final grade will be determined roughly as follows:

Homework  30%

Midterm  30%

Final  40%

Participation in the ECE Seminar on Modern AI:

21st of September: Robert Schapire

26th of October: John Langford

30th of November: Chris Wiggins  Extra  10%

Homework 2 is released and due 10.04.2022.

Homework 3 is released and due 10.18.2022.

The NYU Tandon School values an inclusive and equitable environment for all our students. I hope to foster a sense of community in this class and consider it a place where individuals of all backgrounds, beliefs,ethnicities, national origins, gender identities, sexual orientations, religious and political affiliations, and abilities will be treated with respect. It is my intent that all students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength,and benefit. If this standard is not being upheld, please feel free to speak with me.

I personally will have zero tolerance to acts of racism, sexism, homophobia, xenophobia, or any other known form of discrimination. You get caught; you will face consequences. No exceptions, no excuses.