lectures in Machine Learning

Hi, everybody, I have started posting lectures for my course I am teaching this semester: https://github.com/diefimov/MTH594_MachineLearning I tried to make this course useful for solving practical problems. Mainly I used ideas from these 3 sources: Stanford lectures by Andrew Ng on YouTube: https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599 The book “The elements of Statistical Learning” by T. Hastie, R. Tibshirani and J. Friedman: http://statweb.stanford.edu/~tibs/ElemStatLearn Lectures by Andrew Ng on Coursera: https://www.coursera.org/learn/machine-learning The main feature of my course is that I have adapted the explanation for math students and added ipython notebooks for each lecture. You can easily see how to train the models with scikit-learn and other packages in Python. For the methods that should be explained in more details (like neural networks, Lecture 5) I have added my own simple implementation in Python and…


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