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

Course Description

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.

Textbook

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

Homeworks

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!!!

Course Work and Grading

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%

Tentative Schedule

Week 1 (09.06.2022): (Topic 2) Regression, Empirical Risk Minimization, Least Squares, Higher Order Polynomials, Under-fitting / Over-fitting, Cross-Validation and (Topic 3) Additive Models and Linear Regression, Sinusoids and Radial Basis Functions, Classification, Logistic Regression, Gradient Descent Homework 1 is released and due 09.20.2022.

Week 2 (09.13.2022): (Topic 4) Perceptron, Online & Stochastic Gradient Descent, Convergence Guarantee, Perceptron vs. Linear Regression, Multi-Layer Neural Networks, Back-Propagation, Demo: LeNet, Deep Learning

Week 3 (09.20.2022): (Topic 5) Generalization Guarantees, VC-Dimension, Nearest Neighbor Classification (infinite VC dimension), Structural Risk Minimization, Support Vector Machines Due date for Homework 1.

Homework 2 is released and due 10.04.2022.

Week 4 (09.27.2022): (Topic 6) Kernels and Mappings and (Topic 7) Introduction to Probability Models Week 5 (10.04.2022): (Topic 8) Discrete Probability Models, Independence, Bernoulli Distribution, Text : N a ï v e Bayes, Categorical / Multinomial Distribution, Text: Bag of Words and (Topic 9) Continuous Probability Models, Gaussian Distribution, Maximum Likelihood Gaussian, Sampling from a Gaussian Due date for Homework 2.

Homework 3 is released and due 10.18.2022.

Week 6 (10.18.2022): (Topic 10) Classification with Gaussians, Regression with Gaussians, Principal Components Analysis and (Topic 11) Maximum Likelihood as Bayesian Inference, Maximum A Posteriori,Bayesian Gaussian Estimation

Inclusion Statement

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.

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