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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

DataHack Radio #17: Reinforcement Learning with Professor Balaraman Ravindran

Pranav Dar
Pranav Dar Last Updated : 13 Jun, 2019
5 min read

Introduction

Reinforcement learning is an intriguing but complex topic to get your head around. It’s also one of the most promising skills a data scientist can add to their portfolio. Reinforcement Learning has sprung up some of the biggest ground-breaking developments in the last few years, including powering Google DeepMind’s popular AlphaGo program.

Who better to demystify the aura around this vast field than India’s foremost researcher on Reinforcement Learning? Yes, we’re talking about none other than the eminent Professor Balaraman Ravindran!

Professor B. Ravindran has an incredibly rich background in academic research, headlined by his work in reinforcement learning. His Google Scholar profile shows his research papers have been cited over 2,200+ times!

He has a penchant of explaining the most complex topics in words even beginners are able to grasp. It’s just one of the many reasons his talk at DataHack Summit 2018 was a super hit among our community.

In this podcast, Kunal speaks to Professor Ravi about his background, his interest and research in reinforcement learning, and the intricacies and nuances of this field.

All DataHack Radio episodes are available on the below podcast platforms. Subscribe today!

 

Professor Balaraman Ravindran’s Background

Neural networks were all the rage back in the 1990’s. Given Professor Ravi’s passion for computational models, it was inevitable he would start delving into this subject.

As he explored state-of-the-art neural networks during his Master’s (from IISc), he realized that the approaches were moving away from explaining how humans learn. That led to his foray into the world of reinforcement learning (and we are all really grateful for that!).

He started reading research papers on neuroscience as his fascination with reinforcement learning continued to grow. So what was the state of RL back then? This quote from Professor Ravi sums it up perfectly:

“I was the only person in my Master’s class who was working on Reinforcement Learning.”

One of the biggest drawbacks in India was that there wasn’t a single resource available to learn reinforcement learning. So Professor Ravi along with other researchers wrote a survey paper and emailed it out. This survey picked up pace in the community and even caught the eye of the Oxford press. They asked Professor Ravi and team to write a chapter on RL for their handbook on neural computation (published in 1996).

 

Ph.D in Reinforcement Learning and Working with Andrew Barto

This survey served as Professor Ravi’s entree into his Ph.D, which he successfully completed from the University of Massachusetts. His Ph.D advisor? None other than the great Andrew Barto!

One of the questions Professor Ravi and Andrew Barto wrestled with concerned the human psyche. How are humans so good at learning one problem and transferring it to another task so quickly? The duo attempted to solve this question for tasks that were similar in nature.

If you’re wondering what “similar” here means, you aren’t the only one! Professor Ravi’s research came to the point where he needed to formally define this word in the context of his research. That’s how they came up with the concept of abstract algebra and the mathematics of homomorphisms. A lot of transfer learning frameworks have actually been built on homomorphisms.

Here is his formal definition of similarity:

“Two things are similar if, for everything I can do for situation A, there is a decision in situation B that has similar effects.”

Professor Ravi was kind enough to share his Doctoral dissertation presentation on this topic which you can download here. It is a MUST-READ for anyone pursuing this field.

Later, while working with one his students, Professor Ravi discovered that this notion of similarity in RL was exactly what computer scientists call graphs. A fascinating insight into how the approach works!

“The mathematical notions of similarity that we define are all on an abstract model of the world.”

 

Research at IIT-Madras

Professor Ravi joined IIT-Madras’ faculty following his Ph.D. There he explored multiple areas of research since reinforcement learning had still not picked up steam. He explored domains like Natural Language Understanding and learning on graphs.

Circling back to RL, Professor Ravi has continued to work on homomorphisms at IIT-Madras. Another stream he has been working on is learning complex policies. The question he has been exploring deals with how to design agents that can mimic human thinking (the same question he was pursuing with Andrew Barto).

Hierarchical reinforcement learning frameworks have been another area of interest and research (including a ton of work on attention modeling). Complementing that is deep reinforcement learning (on ATARI games) which really took off in 2014. This gave Professor Ravi more complex domains to work on. He has explained the Deep RL concept using a superb analogy. This section is a MUST-LISTEN for everyone in data science. You will be able to understand the explanation even if you’re a relative beginner in this field.

This section is a nice microcosm of the magic behind Professor B. Ravindran’s teaching methods.

Another direction of research Professor Ravi has been looking at is going beyond rewards. We usually work with rewards when experimenting with a RL approach, right? But there are plenty of other signals apart from this in real-world scenarios. It’s critical to understand this if we are to integrate reinforcement learning in a human-centric world.

 

Professor Ravi’s Work in the Industry

What makes Professor B. Ravindran’s experience unique is that his work isn’t limited to academia and research. He has worked on multiple industry projects, including a few on:

  • Language (NLU)
  • Multi-modal learning
  • Learning with networks

What about RL specific projects? Those do come along, but not as often as one might think. He has worked on RL projects where the optimization component is rolled into the problem (where there’s no pre-defined model).

 

Robert Bosch Centre for Data Science and Artificial Intelligence

Professor Ravi is also the head of the Robert Bosch Centre for Data Science and Artificial Intelligence at IIT-Madras. This was founded in 2017 with a vision to become an internationally renowned centre for data science research, where long-standing fundamental research problems, cutting across disciplines, are targeted and solved.

I highly recommend following their GitHub page to check out their latest work.

 

End Notes

I had the pleasure of meeting Professor Ravi at DataHack Summit 2018. He is a very down-to-earth person with an incredible enthusiasm for this field. He has an infectious joy about him and hearing him talk about RL feels like a dream come true.

All these qualities come out in this episode as well. What an awesome episode with one of the top people in our community. Happy listening and do share your feedback with us below.

DataHack Radio PodcastReinforcement Learning
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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