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.
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/.
If you have specific questions about this course, please contact us at [email protected].
- Understand the principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning
- Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models
- Choose suitable models for different applications
- Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.
Prerequisites
Sponsored by: MITx
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Meet your instructors
Regina Barzilay
Regina Barzilay is a Delta Electronics Professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing, applications of deep learning to chemistry and oncology.
She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. She received her Ph.D. in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.
Tommi Jaakkola
Tommi S. Jaakkola received M.Sc. in theoretical physics from Helsinki University of Technology and Ph.D. from MIT in computational neuroscience. He joined MIT faculty 1998 and he is now the Thomas Siebel Professor in EECS and IDSS at MIT.
His research covers theory, algorithms, and applications of machine learning, from statistical inference and estimation to natural language processing, computational biology, as well as recently machine learning for chemistry. His awards include Sloan research fellowship, AAAI Fellow, and many publication awards across the research areas.
Karene Chu
Karene Chu received her Ph.D. in mathematics from the University of Toronto in 2012. Since then she has been a postdoctoral fellow first at the University of Toronto/Fields Institute, and then at MIT, with research focus on knot theory. She has taught single and multi-variable calculus, and linear algebra at the University of Toronto where she received a teaching award.
Course Features
- Lectures 2
- Quizzes 0
- Duration 210 hours
- Skill level All levels
- Language English
- Students 78468
- Assessments Yes
