Linear Algebra for Machine Learning Workshop
Linear Algebra, Optimization & Statistics is base of all machine learning. This workshop will cover required linear algebra for machine learning in a hands on way through short code examples. We will cover basic theory, interesting applications and the big picture.
Outline/Structure of the Workshop
This workshop will be in three parts. Overall idea is to first motivate audience on why linear algebra is important, give a big picture first rather than tiny details. Then we cover real applications through coding so that audience understands most important ideas in this topic. Lastly, we will cover enough details on how to study and apply it further.
Introduction / Getting feet wet
 Why is linear algebra so important and pervasive in machine learning.
 Basic code examples, linear algebra 101
 Motivation for some topics in linear algebra, why are they important (solving system, basis, orthogonalization, eigen values and eigen vectors, SVD)
Practical Applications through Coding
 Simple application: Regression models training and scoring
 Intermediate applications: Applications in optimization methods
 Advanced applications: Singular Value Decomposition, Principal component analysis, application to recommendations systems.
Practical Applications Discussion
In this section, depending on time we will discuss even more applications, some of them from other engineering disciplines (like control theory, signal processing) and physics and explain how they related to data science and machine learning. The idea is to go beyond the currently hyped topics in machine learning and explain the all encompassing reach of Linear Algebra in engineering.
Study Resources, Question Answers
 How to study further, books and courses which helped me so far.
 Open question answer session
Learning Outcome
By the end of workshop, you would
 know why linear algebra is important for machine learning.
 understand how the most important ideas/algorithms in linear algebra work and how to apply them in your work.
 understand how linear algebra relates to machine learning through hands on coding examples.
 increase your curiosity and respect for pervasiveness of linear algebra in data science.
 know which resources can help you study further.
Target Audience
Beginners in machine learning field who want to learn the maths/linear algebra in an intuitive way.
Prerequisites for Attendees
 High school maths
 Familiarity with basic machine learning like linear regression will help but not mandatory.
 Programming knowledge (given below)
 Simple imperative programming like assigning to values to variables, printing things
 Loops, conditions
 Reading and writing to/from files.
 Basic data structures and data types: floating point numbers, arrays/vectors, maps
 Familiarity with Python will help. There are many tutorials available online. This is a good place to start.
Links
 I gave a talk in this conference on bayesian methods in data analysis.
 I conducted this workshop on maths for machine learning last year.
schedule Submitted 10 months ago
People who liked this proposal, also liked:

keyboard_arrow_down
Favio Vázquez  Agile Data Science Workflows with Python, Spark and Optimus
480 Mins
Workshop
Intermediate
Cleaning, Preparing , Transforming and Exploring Data is the most timeconsuming and least enjoyable data science task, but one of the most important ones. With Optimus we’ve solve this problem for small or huge datasets, also improving a whole workflow for data science, making it easier for everyone. You will learn how the combination of Apache Spark and Optimus with the Python ecosystem can form a whole framework for Agile Data Science allowing people and companies to go further, and beyond their common sense and intuition to solve complex business problems.

keyboard_arrow_down
Vishal Gokhale  Fundamental Math for Data Science
480 Mins
Workshop
Beginner
By now it is evident that a solid math foundation is indispensable if one has to get into Data science in an honesttogoodness way. Unfortunately, for many of us math was just a means to get better scores at schoollevel and never really a means to understand the world around us.
That systemic failure (education system) causes many of us to feel a “gap” when learning data science concepts. It is high time that we acknowledge that gap and take remedial action.The purpose of the workshop is to develop an intuitive understanding of the concepts.
We let go the fear of rigorous notation and embrace the rationale behind it.
The intended key take away for participants is confidence to deal with math. 
keyboard_arrow_down
Saurabh Deshpande  Introduction to reinforcement learning using Python and OpenAI Gym
90 Mins
Workshop
Advanced
Reinforcement Learning algorithms becoming more and more sophisticated every day which is evident from the recent win of AlphaGo and AlphaGo Zero (https://deepmind.com/blog/alphagozerolearningscratch/ ). OpenAI has provided toolkit openai gym for research and development of Reinforcement Learning algorithms.
In this workshop, we will focus on introduction to the basic concepts and algorithms in Reinforcement Learning and hands on coding.
Content
 Introduction to Reinforcement Learning Concepts and teminologies
 Setting up OpenAI Gym and other dependencies
 Introducing OpenAI Gym and its APIs
 Implementing simple algorithms using couple of OpenAI Gym Environments
 Demo of Deep Reinforcement Learning using one of the OpenAI Gym Atari game

keyboard_arrow_down
Abhijith  Computational Machine Learning
480 Mins
Workshop
Intermediate
You have been hearing about machine learning (ML) and artificial intelligence (AI) everywhere. You have heard about computers recognizing images, generating speech, natural language, and beating humans at Chess and Go.
The objectives of the workshop:

Learn machine learning, deep learning and AI concepts

Provide handson training so that students can write applications in AI

Provide ability to run real machine learning production examples

Understand programming techniques that underlie the production software
The concepts will be taught in Julia, a modern language for numerical computing and machine learning  but they can be applied in any language the audience are familiar with.
Workshop will be structured as “reverse classroom” based laboratory exercises that have proven to be engaging and effective learning devices. Knowledgeable facilitators will help students learn the material and extrapolate to custom real world situations.


keyboard_arrow_down
Sai Charan J  Self Learning  Data Science
45 Mins
Workshop
Beginner
For people from a nontechnical background, I recommend formal academic programs. And then raising the bar comes datadriven scientist  Self Taught Data Scientist! These people are trendsetters, go way deep & play with data. They love data crunching & are seen solving realtime problems!
If that's you, then let's wave our hands!

keyboard_arrow_down
Saurabh Deshpande  Introduction to Natural Language Processing using Python
90 Mins
Workshop
Intermediate
Python ecosystem for Natural language processing has evolved in last decade and rich set of open source tools and data sets are now available.
In this session, we will go over basics of Natural language processing along with sample code demonstration and hands on tutorials using following famous python libraries,
 NLTK : One of the oldest and famous library for natural language analysis for researchers
 Stanford CoreNLP : Production ready NLP library. (Written in java but has many open source python wrappers)
 SpaCy: Comparatively new python NLP toolkit marketed as 'Industrial Strength' python library.
Session will introduce the various use cases and basic concepts related to the natural language processing with demo and hands on tutorials
Following NLP fundamentals will be discussed,
 Syntax Vs. Semantics
 Regular Expressions (Demo and Hands on)
 Word Embeddings (Demo and Hands on)
 Word Tokenization (Demo and Hands on)
 Part of Speech Tagging (Demo and Hands on)
 Text Similarity (Demo and Hands on)
 Text Summarization
 Named Entity Recognition ((Demo and Hands on))
 Sentiment Analysis (Demo and Hands on)

keyboard_arrow_down
Venkatraman J  Hands on Data Science. Get hands dirty with real code!!!
45 Mins
Workshop
Intermediate
Data science refers to the science of extracting useful information from data. Knowledge discovery in data bases, data mining, Information extraction also closely match with data science. Supervised learning,Semi supervised learning,Un supervised learning methodologies are out of Academia and penetrated deep into the industry leading to actionable insights, dashboard driven development, data driven reasoning and so on. Data science has been the buzzword for last few years in industry with only a handful of data scientists around the world. The industry needs more and more data scientists in future to solve problems using statistical techniques. The exponential availability of unstructured data from the web has thrown huge challenges to data scientists to exploit them before driving conclusions.
Now that's overload of information and buzzwords. It all has to start somewhere? Where and how to start? How to get hands dirty rather than just reading books and blogs. Is it really science or just code?. Let's get into code to talk data science.
In this workshop i will show the tools required to do real data science rather than just reading by building real models using Deep neural networks and show live demo of the same. Also share some of the key data science techniques every aspiring data scientist should have to thrive in the industry.
Public Feedback
Note for organisers, this will be approximately 33.5 hours workshop. 480 minutes is too long for this workshop, but I couldn't find nearest matching time in the selectors.
Thanks. I've updated the proposal to 240 mins (4 hours.)