Introduction to Machine Learning for Coders

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Overview Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.

Practical Deep Learning For Coders

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Overview Learn how to build state of the art models without needing graduate-level mathbut also without dumbing anything down. This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step onelearning how to get a GPU server online suitable for deep learningand go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems.

Cutting Edge Deep Learning for Coders

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Overview Welcome to thenew 2018 editionof fast.ai's second 7 week course,Cutting Edge Deep Learning For Coders, Part 2, where you'll learn the latest developments in deep learning, how to read and implement new academic papers, and how to solve challenging end-to-end problems such as natural language translation. You'll develop a deep understanding of neural network foundations, the most important recent advances in the fields, and how to implement them in theworld's fastest deep learning libraries, fastai and pytorch.

Computational Linear Algebra for Coders

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Overview This course is focused on the question: How do we do matrix computations with acceptable speed and acceptable accuracy? The course is taught in Python with Jupyter Notebooks, using libraries such as scikit-learn and numpy for most lessons, as well as numba and pytorch in a few lessons.

Statistical Machine Learning

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Overview Statistical Machine Learning,10-702/36-702, is a second graduate level course in advanced machine learning. The term statistical in the title reflects the emphasis on statistical theory and methodology. The course combines methodology with theoretical foundations. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own research. The course includes topics in statistical theory that are important for researchers in machine learning, including nonparametric theory, consistency, minimax estimation, and concentration of measure.

Unsupervised Learning: From Big Data to Low-Dimensional Representations

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Overview In the era of data deluge, the development of methods for discovering structure in high-dimensional data is becoming increasingly important. This course will cover state-of-the-art methods from algebraic geometry, sparse and low-rank representations, and statistical learning for modeling and clustering high-dimensional data. The first part of the course will cover methods for modeling data with a single low-dimensional subspace, such as PCA, Robust PCA, Kernel PCA, and manifold learning techniques. The second part of the course will cover methods for modeling data with multiple subspaces, such as algebraic, statistical, sparse and low-rank subspace clustering techniques. The third part of the course will cover applications of these methods in image processing, computer vision, and biomedical imaging.

Cognitive Robotics

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Overview This is a class about applying autonomy to real-world systems. The overarching theme uniting the many different topics in this course will center around programming a cognitive robotic. This class takes the approach of introducing new reasoning techniques and ideas incrementally. We start with the current paradigm of programming you're likely familiar with, and evolve it over the semestercontinually adding in new features and reasoning capabilitiesending with a robust, intelligent system. These techniques and topics will include algorithms for allowing a robot to: Monitor itself for potential problems (both observable and hidden), scheduling tasks in time, coming up with novel plans to achieve desired goals over time, dealing with the continuous world, collaborating with other (autonomous) agents, dealing with risk, and more.

Python Tutorials for Absolute Beginners

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Overview Learn Python programming with this Python tutorial for beginners!