### Graphs and Network Algorithms from Scratch

Sept 16, 2019 - 5am-9am (PST)

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).

### Deep Learning From Scratch

Sept 24, 2019

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.

### Deep Learning From Scratch

Sept 30, 2019 - 5am-9am (PST)

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.

### Time Series Data Processing and Modeling

Oct 14, 2019 - 7am-11am (PST)

The availability of large quantity of cheap sensors brought forth by the so called “Internet of Things” has resulted in an explosion of the amounts of time varying data. Understanding how to mine, process and analyze such data will only to become an ever more important skill in any data scientists toolkit.

In this lecture, we will work through the entire process of how to analyze and model time series data, how to detect and extract trend and seasonality effects and how to implement the ARIMA class of forecasting models. Both real and synthetic datasets will be used to illustrate the different kinds of models and their underlying assumptions.

### Applied Probability Theory from Scratch

Oct 30, 2019 - 5am-9am (PST)

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.

### Natural Language Processing from Scratch

Nov 11, 2019 - 7am-11am (PST)

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.

### Graphs and Network Algorithms from Scratch

Nov 18, 2019 - 5am-9am (PST)

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).

### Data Visualization with matplotlib and seaborn

Dec 4, 2019 - 5am-9am (PST)

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 correctlyinterpreted 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.

### Deep Learning From Scratch

Dec 11, 2019 - 5am-9am (PST)

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.