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Applied Probability Theory from Scratch
Oct 30, 2019 - 5am-9am

Recent advances in Machine Learning and Artificial Intelligence have result in a great deal of attention and interest in these two areas of Computer Science and Mathematics. Most of these advances and developments have relied in stochastic and probabilistic models, requiring a deep understanding of Probability Theory and how to apply it to each specific situation

In this lecture we will cover in a hands-on and incremental fashion the theoretical foundations of probability theory and recent applications such as Markov Chains, Bayesian Analysis and A/B testing that are commonly used in practical applications in both industry and academia.

 
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Natural Language Processing from Scratch
Nov 11, 2019 - 7am-11am

The rise of online social platforms has resulted in an explosion of written text in the form of blogs, posts, tweet, wiki pages, etc. This new wealth of data provides a unique opportunity to explore natural language in its many forms, both as a way of automatically extracting information from written text and as a way of artificially producing text that looks natural.

In this video we will introduce viewers to natural language processing from scratch. Each concept is introduced and explained through coding examples using nothing more than just plain Python and numpy. In this way, viewers will learn in depth about the underlying concepts and techniques instead of just learning how to use a specific NLP library.

 
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Graphs and Network Algorithms from Scratch
Nov 18, 2019 - 5am-9am

Trees, graphs and networks are fundamental data structures that underlie much of the recent developments in data science and computer science algorithms. Technologies and applications like social networks, cloud and distributed computing, cryptocurrencies and traffic routing and directions all rely on the proper use of graph concepts.

In this course we will build, step by step, a mini toolkit of network representations and algorithms that will allow students to understand the fundamental ideas and concepts that lie at the base of state of art algorithms (such as PageRank and recommendation systems), technologies (such as graph databases) and tools (like web crawlers).

 
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Data Visualization with matplotlib and seaborn
Dec 4, 2019 - 5am-9am

As David McCandless famously said “Information Visualization is a form of knowledge compression”. In particular, it is a way of compressing information in a visual way that can be easily and correctly interpreted by the visual system in our brains.

In this tutorial we will discuss the way in which our eyes and visual cortex process colors and shapes and how we may use it to our advantage. Ideas and concepts will be presented in an intuitive and practical way while providing references for the more technical descriptions and explanations available in the relevant scientific literature.

 
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Deep Learning From Scratch
Dec 11, 2019 - 5am-9am

Over the past few years we have seen a convergence of two large-scale trends: Big Data and Big Compute. The resulting combination of large amounts of data and abundant CPU (and GPU) cycles has brought to the forefront and highlighted the power of neural network techniques and approaches that were once thought to be too impractical.

Deep Learning, as this new wave of interest has come to be known, has made impressive and unprecedented progress on applications as diverse as Natural Language Processing, Machine Translation, Computer Vision, Robotics, etc. In this lecture, students will learn, in a hands-on way, the theoretical foundations and principal ideas underlying this burgeoning field. The code structure of the implementations provided is meant to closely resemble the way the state of the art deep learning libraries Keras is structured so that by the end of the course, students will be prepared to dive deeper into the deep learning applications of their choice.