About this course

If you have specific questions about this course, please contact us at sds-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 the MITx 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/.

At a Glance:
Institution: MITx
Subject: Computer Science
Level: Advanced
Prerequisites:
6.00.1x or proficiency in Python programming
6.431x or equivalent probability theory course
College-level single and multi-variable calculus
Vectors and matrices
Associated programs:
MicroMasters® Program in Statistics and Data Science (General Track)
MicroMasters® Program in Statistics and Data Science (Methods Track)
MicroMasters® Program in Statistics and Data Science (Social Sciences Track)
MicroMasters® Program in Statistics and Data Science (Time Series and Social Sciences Track)
Language: English
Video Transcript: English
Associated skills:Sales, Deep Learning, Algorithms, Data Science, Machine Learning Algorithms, Recommender Systems, Machine Learning, Experimentation, Artificial Neural Networks, Physics, Forecasting, Python (Programming Language), Prediction, Reinforcement Learning, Statistics, Linear Model, Consumer Behaviour, Support Vector Machine

What You’ll Learn:
About this course

If you have specific questions about this course, please contact us at sds-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 the MITx 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/.