Renzo Silva

Framing the ML problem, data collection and generalization, data and loss, regularization and logistic regression, classification, natural language processing, artificial neural networks, GANs and Kmeans, and reinforcement learning.

Basic Machine Learning models and the theories behind these models

Machine learning models. Some python practices.

Basic ML framework, basic nlp, neutral network concepts, python technics.

The concepts behind lots of machine learning algorithms, and how to apply them in Python.

The standard process of ML application in data analysis and a comprehensive coverage of various machine learning models.

During the course we learned the concepts of supervised, unsupervised and semi-supervised learning and how these concepts can be applied to finance. We covered most of the common machine learning algorithms and implemented them in Python.

Basic ML concepts - different ML models, data preprocessing, evaluation metrics, NLP, etc. One thing to point out is that the class touches on a broad range of topics but did not dive in too deeply in any of the them. Would highly recommend this class if student wants a general intro to ML, but not so much if they want to focus on a particular topic.

All assignments are programming exercises in Python, but this is not taught in class. In order to be learn the programming skills necessary to implement the topics taught in lecture, it was necessary to self-teach or attend the TA's office hours.

If you want to learn some basic concepts and frameworks in ML, you can take this course.