Machine Learning with Python: from Linear Models to Deep Learning

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.

If you have specific questions about this course, please contact us atsds-mm@mit.edu.

Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.

As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.

In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:

• Representation, over-fitting, regularization, generalization, VC dimension;
• Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
• On-line algorithms, support vector machines, and neural networks/deep learning.

Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/

• Linear classifiers, separability, perceptron algorithm
• Maximum margin hyperplane, loss, regularization
• Stochastic gradient descent, over-fitting, generalization
• Linear regression
• Recommender problems, collaborative filtering
• Non-linear classification, kernels
• Learning features, Neural networks
• Deep learning, back propagation
• Recurrent neural networks
• Generalization, complexity, VC-dimension
• Unsupervised learning: clustering
• Generative models, mixtures
• Mixtures and the EM algorithm

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