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…

Link to Full Article: lectures in Machine Learning

Pin It on Pinterest

Share This

Join Our Newsletter

Sign up to our mailing list to receive the latest news and updates about homeAI.info and the Informed.AI Network of AI related websites which includes Events.AI, Neurons.AI, Awards.AI, and Vocation.AI

You have Successfully Subscribed!