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<!DOCTYPE html>
<html>
<head>
<title>SHALA-2020</title>
<meta property="og:image" content="https://shala2020.github.io/images/cp.jpg" />
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
<!-- extra -->
<!-- bootstrap -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css">
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css">
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css">
<link href='http://fonts.googleapis.com/css?family=Roboto:400,300' rel='stylesheet' type='text/css'>
<link rel="stylesheet" type="text/css" href="css/index.css" />
<!-- For social Handles -->
<link href="//maxcdn.bootstrapcdn.com/font-awesome/4.1.0/css/font-awesome.min.css" rel="stylesheet">
<link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.5/css/bootstrap.min.css" rel="stylesheet"
integrity="sha256-MfvZlkHCEqatNoGiOXveE8FIwMzZg4W85qfrfIFBfYc= sha512-dTfge/zgoMYpP7QbHy4gWMEGsbsdZeCXz7irItjcC3sPUFtf0kuFbDz/ixG7ArTxmDjLXDmezHubeNikyKGVyQ=="
crossorigin="anonymous">
</head>
<body>
<div id="header">
<!-- <a href="http://www.iitb.ac.in/" target="_blank">
<img src="images/IITB-logo.png" style="height:50px; float: left; margin-left: 20px;">
</a>
<a href="http://www.iitb.ac.in/">
<img src="images/IITB-logo.png" style="height:50px; float: right; margin-right: 20px;">
</a> -->
<h1><b>MastAI ki paathSHALA</b></h1>
<div class='text-center'>
<h2>Have fun Staying Home And Learning AI</h2>
</div>
<div class='text-center'>
<h3>A course on Data Science, Machine Learning, and Deep Learning</h3>
<h4>Summer 2020</h3>
</div>
<div style="clear:both;"></div>
</div>
<div
style="background-color: linen; border: 3px solid darkred; margin: 4px; padding: 2px; font-weight: bold; text-align: center;">
Classes will resume from May 25 with a new schedule. This week, please catch up with the recorded <a
href="https://www.youtube.com/channel/UCobe_Yc7nV6kux94A5doIiA/" target="_blank">lectures</a> and
assignments. Check the section <a href="#break">Interim break</a> for extra list of things to do.
</div>
<!-- <marquee behavior="scroll" direction="left"
style="background-color: linen; margin: 4px; padding: 2px; font-weight: bold;">
First lecture on April 15 at 9pm IST, pre-work and assignment released.
</marquee> -->
<div id="teaser">
<div>
<iframe width="560" height="370" src="https://www.youtube.com/embed/xHXzm0P0Jx0" frameborder="0"
allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen></iframe>
<!--<img src="images/ML.jpeg" style="height:370px; width: 800px;">-->
</div>
</div>
<div class="container sec">
<div class="topnav" id="myTopnav">
<a href="#about" class="active">About</a>
<a href="#topics">Topics</a>
<a href="#pedagogy">Pedagogy</a>
<a href="#schedule">Schedule</a>
<a href="#prerequisites">Prerequisites</a>
<a href="#team">Team</a>
<a href="#signup">Signup</a>
<a href="#faq">FAQs</a>
<a href="#contact">Contact</a>
<a href="javascript:void(0);" class="icon" onclick="myFunction()">
<i class="fa fa-bars"></i>
</a>
</div>
</div>
<div class="container" style="margin-top: -20px; margin-bottom: 10px">
<!-- <hr> -->
<div class="text-center center-block">
<a href="https://www.facebook.com/mastAIkipaathSHALA/" target="_blank"><i id="social-fb"
class="fa fa-facebook-square fa-3x social"></i></a>
<a href="https://www.youtube.com/channel/UCobe_Yc7nV6kux94A5doIiA/" target="_blank"><i id="social-yb"
class="fa fa-youtube-square fa-3x social"></i></a>
<a href="https://www.instagram.com/shala.2020/" target="_blank"><i id="social-in"
class="fa fa-instagram fa-3x social"></i></a>
<!-- <a href="mailto:[email protected]" target="_blank"><i id="social-em" class="fa fa-envelope-square fa-3x social"></i></a> -->
</div>
<!-- <hr> -->
</div>
<div class="sechighlight">
<div class="container sec">
<h2 id="about">ABOUT</h2>
<div id="coursedesc">
<p> During these months of social distancing and lockdown, a lot of college students are not able to
attend classes or go for internships where they could be learning practical tech skills. We, a team
of volunteers comprising professors, industry professionals, and students working in data science,
machine learning, and deep learning have gotten together to offer a course to students of
engineering colleges on these topics. There is no fee for this course, and this is our way of
contributing to
the Indian and world community in these trying times.</p>
<p>We would like the course to be full of rewarding practical knowledge for the diligent students, which
we will achieve by giving rigorous programming exercises and mini-projects to apply the concepts
learned during the course. We will attempt to make sure that all diligent students who have the
requisite background knowledge are able to make progress on the course by getting their doubts
cleared in
interactive video calls. Please see more details in the section about <a href="#topics"> Topics
</a> and <a href="#pedagogy"> Pedagogy</a>. The course is going to be rigorous but fun and rewarding
to the diligent students who finish the entire course. However, between consecutive topics, we would
allow
any student to drop the course if one no longer wishes to continue given any circumstances.</p>
</div>
</div>
</div>
<div class="thirdhighlight">
<div class="container sec">
<h2 id="topics">TOPICS</h2>
<div id="coursedesc">
<p> <b>Data Science (DS)</b>: Getting started, Basic data understanding, Improving plots, Basic
statistics.</p>
<p> <b>Machine Learning (ML)</b>: Introduction to ML, Decision trees, Bayesian decision theory, Linear
models, Kernelization, Feature selection and engineering, Dense and shallow neural networks,
Advanced topics in neural networks, Clustering, Model Explainability.</p>
<p> <b>Deep Learning (DL) for Vision</b>: Introduction to CNNs, Advanced conv nets, Semantic
segmentation, Object detection, Instance segmentation, Few-shot learning, Metric learning,
Generative Adversarial Networks (GANs), Variational Autoencoder (VAE).
</p>
<p> <b>Deep Learning for Natural Language Processing (NLP)</b>: Word embeddings, Language modeling,
Simple applications of LSTMs, Advanced applications of LSTMs - 1, Relationship extraction, Advanced
applications of LSTMs - 2, Advanced applications of LSTMs - 3, Beyond simple word embeddings, Beyond
LSTMs, Chat-bot making.</p>
<p><b>Miscellaneous</b>: Graph conv nets for NLP, Knowledge graphs, Reinforcement Learning. </p>
<p> <b>Practical Implementation of ML Models</b>: Deployment - 1, Deployment - 2, Deployment - 3,
Front-end and logistics, Making APIs, Winning Kaggle, Compute and bandwith considerations, When to
use which framework.
</p>
</div>
<h2 id="pedagogy">PEDAGOGY</h2>
<div id="coursedesc">
<p>The course will be divided in small topics, which will require one or two days of intense work each.
There will be approximately three topics covered each week for approximately eight weeks. The
following will be the <u>components of each topic</u>:
<ul>
<li>Pre-work: Links to online text and videos will be shared.
</li>
<li>Pre-quiz: A quiz on basic concepts will be given to assess the student’s readiness for the
module.</li>
<li>Online interactive video lecture: The material in the pre-work will be covered, doubts will
be cleared, and more advanced insights will be provided.</li>
<li>Assignment release: Assignment for the module will be released on kaggle as an in-class
competition.</li>
<li>Assignment help session: A second online interactive video session will be conducted for
each session to clear any doubts about the assignment.</li>
<li>Assignment submission.</li>
<li>Release of instructor’s and the best of students’ solutions.</li>
</ul>
</p>
<p>After a group of modules, project statements will be released.</p>
<p>Please note that there will be no formal certificate given to the students for completing the course
assignments. The best projects and assignments will be mentioned on the course website for all to
see. Students are encouraged to make their <a href="https://github.com/" target="_blank">GitHub</a>
portfolios and point to the course webpage, which
will help them immensely for recruitment.</p>
</div>
<h2 id="schedule">SCHEDULE</h2>
<p> For detailed schedule, click <a
href="https://docs.google.com/spreadsheets/d/e/2PACX-1vR2UGMjeIZf0BEDGSoyZEo-6XuLX_Xy50Ozs3lSqnATcbi68kgPUhdH1VyIXpDAeI_ZYfW1pzsymTP4/pubhtml?gid=1184439562&single=true"
target="_blank"><strong>here</strong></a>
</p>
<p><b>Note</b>: Interactive sessions will start at 9pm Indian Standard Time (IST).
<!-- Look out for an invitation to
<a href="https://campuswire.com/" target="_blank">CampusWire</a> in your email, which will be our
platform for keeping in touch with the students. -->
</p>
<!-- <center><a class="btn btn-success btn-lg" href="notes.html" target="_blank">Click here for lecture notes</a></center> -->
<!-- ######################### Material upload ################################## -->
<div class="wrap">
<div class="module-header">Module 0: Introduction to the course</div>
<div class="materials-item">
<div>
Pre-work: <a href="https://mml-book.github.io/book/mml-book.pdf" style="display: inline"
target="_blank">Mathematics for Machine Learning by Deisenroth, Faisal, and Ong</a> Section:
2.1-2.3, 3.1-3.4, 5.1-5.2, 6.1-6.5, 7.1, <a href="https://www.learnpython.org/"
style="display: inline" target="_blank">Python Tutorials</a>
</div>
<div class="kw">
<a href="https://www.youtube.com/watch?v=u5kK52JZBNU" style="display: inline"
target="_blank">Watch the kickoff session</a>
</div>
</div>
<div class="module-header">Module 1: Data Science</div>
<div class="materials-item">
<!-- <a href="classification/">Image Classification: Data-driven Approach, k-Nearest Neighbor, train/val/test splits </a> -->
<div>
<b>Getting started:</b> Google Colab, GPU service, Useful Python libraries, GitHub <br>
Prerequisites: Python data structure, Loops, Classes, Linear Algebra <br>
Pre-work: <a href="https://www.youtube.com/watch?v=inN8seMm7UI" style="display: inline"
target="_blank">Google Colaboratory-1</a>, <a
href="https://colab.research.google.com/notebooks/intro.ipynb" style="display: inline"
target="_blank">Google Colaboratory-2</a>, <a
href="https://docs.scipy.org/doc/numpy/user/quickstart.html" style="display: inline"
target="_blank">NumPy</a>
</div>
<div class="kw">
<a href="https://docs.google.com/forms/d/e/1FAIpQLSf6PT_nt69TO2k7PKKiFMrvtIe9sUsxNk4TRxVJx9TvURxExA/viewform?usp=sf_link"
style="display: inline" target="_blank">Pre-Quiz</a> | <a
href="https://www.youtube.com/watch?v=zKSczEN4lks" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://colab.research.google.com/drive/1qCGe_9ohuuPWGLwKY6sQGnliRGmGix6q"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://www.youtube.com/watch?v=RXPDzGzbmAQ" style="display: inline"
target="_blank">Tutorial</a> | <a
href="https://docs.google.com/forms/d/e/1FAIpQLSdTuDWmV--LKdcquxK47sql7ZF35NDBB1rs7MFJ22emBW9tsQ/viewform?usp=sf_link"
style="display: inline" target="_blank">Quiz</a>
</div>
</div>
<div class="materials-item">
<div>
<b>Basic data understanding:</b> Data science, Central tendency, Plots, Cumulative distribution
<br>
Prerequisites: Identifying plots and basics of statistics, Importing packages, Defining
data frames <br>
Pre-work: <a
href="https://statistics.laerd.com/statistical-guides/measures-central-tendency-mean-mode-median.php"
style="display: inline" target="_blank">Measures of central tendency</a>, <a
href="https://www.youtube.com/watch?v=SzZ6GpcfoQY" style="display: inline"
target="_blank">Statistics fundamentals</a>, <a
href="https://www.youtube.com/watch?v=qBigTkBLU6g" style="display: inline"
target="_blank">Histograms</a>
</div>
<div class="kw">
<a href="https://forms.gle/7VDtBBPy4WWgNQe87" style="display: inline"
target="_blank">Pre-Quiz</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Google_Colab_Notebooks/DataScience/L2"
style="display: inline" target="_blank">Notebook</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/DataScience/L2"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=HLnFJEWYYz0" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/DataScience/L2"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/DataScience/L2/Solution"
style="display: inline" target="_blank">Solution</a> | <a
href="https://www.youtube.com/watch?v=aXpjh-KfiD0" style="display: inline"
target="_blank">Tutorial</a> | <a href="https://forms.gle/dswwWcfXkEtUgjt38"
style="display: inline" target="_blank">Quiz</a>
</div>
</div>
<div class="materials-item">
<div>
<b>Improving plots:</b> Data science, Central tendency, Plots, Cumulative distribution <br>
Prerequisites: Different types of plots, How to customize plots <br>
Pre-work: <a
href="https://chartio.com/learn/charts/essential-chart-types-for-data-visualization/"
style="display: inline" target="_blank">Chart Types for Data Visualization</a>, <a
href="https://visme.co/blog/types-of-graphs/" style="display: inline" target="_blank">Types
of Graphs</a>
</div>
<div class="kw">
<a href="https://forms.gle/JLfSfe3DnD5CfiQ5A" style="display: inline"
target="_blank">Pre-Quiz</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Google_Colab_Notebooks/DataScience/L3"
style="display: inline" target="_blank">Notebook</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/DataScience/L3"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=8NEQud1T2Ik" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/DataScience/L3"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/DataScience/L3/Solution"
style="display: inline" target="_blank">Solution</a> | <a
href="https://www.youtube.com/watch?v=2-I3hqnOJmI" style="display: inline"
target="_blank">Tutorial</a> | Quiz
</div>
</div>
<div class="materials-item">
<div>
<b>Basic statistics:</b> Maximum likelihood estimation, sufficient statistics, null hypothesis
testing, t-test, Wilcoxon rank test <br>
Prerequisites: Conditional probability, Probability distributions and moments <br>
Pre-work: <a href="https://www.khanacademy.org/math/ap-statistics/random-variables-ap"
style="display: inline" target="_blank">Random variables</a>, <a
href="https://www.youtube.com/watch?v=XepXtl9YKwc" style="display: inline"
target="_blank">Maximum likelihood estimation</a>, <a
href="https://statistics.laerd.com/statistical-guides/hypothesis-testing.php"
style="display: inline" target="_blank">Hypothesis testing</a>, <a
href="https://towardsdatascience.com/statistical-tests-when-to-use-which-704557554740"
style="display: inline" target="_blank">Statistical tests</a>
</div>
<div class="kw">
<a href="https://forms.gle/p9f7Qs3bjgFhzCrr8" style="display: inline"
target="_blank">Pre-Quiz</a> | Notebook | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/DataScience/L4"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=JMDlku2peAg" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/DataScience/L4"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/DataScience/L4/Solution"
style="display: inline" target="_blank">Solution</a> | <a
href="https://www.youtube.com/watch?v=hL5i1VQWEfg" style="display: inline"
target="_blank">Tutorial</a> | Quiz
</div>
</div>
<div class="module-header">Module 2: Machine Learning</div>
<div class="materials-item">
<div>
<b>Introduction to ML:</b> Machine learning problems, parameter vs. hyperparameter, overfitting,
training, validation, testing, cross-validation,
regularization
<br>
Prerequisites: Linear algebra, basic statistics, data visualization, encoding features <br>
Pre-work: <a
href="https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer"
style="display: inline" target="_blank">Theory of machine learning</a>, <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Resources/ML"
style="display: inline" target="_blank">Basics of machine learning</a>
</div>
<div class="kw">
<a href="https://forms.gle/qRmdReSNyBkUwt9k9" style="display: inline"
target="_blank">Pre-Quiz</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Google_Colab_Notebooks/MachineLearning/L1"
style="display: inline" target="_blank">Notebook</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/MachineLearning/L1"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=EgGFSUiNkcA" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L1"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L1/Solution"
style="display: inline" target="_blank">Solution</a> | <a
href="https://www.youtube.com/watch?v=J2-rmft5_58" style="display: inline"
target="_blank">Tutorial</a> | Quiz
</div>
</div>
<div class="materials-item">
<div>
<b>Decision Trees:</b> Definition of a decision tree, metrics of impurity, greedy algorithm to
split a node, tree depth and pruning, ensemble of
trees (random forest)<br>
Prerequisites: Intro to ML, supervised learning, unsupervised learning, classification,
regression <br>
Pre-work: <a href="http://www.stats.ox.ac.uk/~flaxman/HT17_lecture13.pdf"
style="display: inline" target="_blank">Classification and Regression Trees</a>, <a
href="https://www.youtube.com/watch?v=J4Wdy0Wc_xQ" style="display: inline"
target="_blank">Random Forests</a>
</div>
<div class="kw">
<a href="https://forms.gle/xmn4axyLxTLRYQGB8" style="display: inline"
target="_blank">Pre-Quiz</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Google_Colab_Notebooks/MachineLearning/L2"
style="display: inline" target="_blank">Notebook</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/MachineLearning/L2"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=hE0TgoPRX6s" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L2"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L2/Solution"
style="display: inline" target="_blank">Solution</a> | <a
href="https://www.youtube.com/watch?v=-4aJ-ZmV0U8" style="display: inline"
target="_blank">Tutorial</a> | Quiz
</div>
</div>
<div class="materials-item">
<div>
<b>Bayesian decision theory:</b> Bayes rule: Prior, likelihood, posterior, evidence,
Gaussian density, sufficient statistics, maximum likelihood derivation for mean and
covariance<br>
Prerequisites: Maximum likelihood estimation, conditional probability, linear algebra
regression <br>
Pre-work: <a
href="https://www.khanacademy.org/partner-content/wi-phi/wiphi-critical-thinking/wiphi-fundamentals/v/bayes-theorem"
style="display: inline" target="_blank">Bayes' theorem</a>, <a
href="https://www.mathsisfun.com/data/bayes-theorem.html" style="display: inline"
target="_blank">Examples of Bayes' theorem</a>, <a
href="https://www.youtube.com/watch?v=iYiOVISWXS4" style="display: inline"
target="_blank">Gaussian distribution</a>
</div>
<div class="kw">
<a href="https://forms.gle/MfyXNxxSGGSgAkJw7" style="display: inline"
target="_blank">Pre-Quiz</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Google_Colab_Notebooks/MachineLearning/L3"
style="display: inline" target="_blank">Notebook</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/MachineLearning/L3"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=fz_EQYeKje4" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L3"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L3/Solution"
style="display: inline" target="_blank">Solution</a> | <a
href="https://www.youtube.com/watch?v=9tGT-eEay5g" style="display: inline"
target="_blank">Tutorial</a> | Quiz
</div>
</div>
<div class="materials-item">
<div>
<b>Linear models:</b> linear regression and its analytical solution, loss function,
gradient descent and learning rate, logistic regression and its cost,
SVM: hinge loss with L2 penalty<br>
Prerequisites: Derivatives, chain rule, partial derivative, convex functions
regression <br>
Pre-work: <a href="https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN"
style="display: inline" target="_blank">Machine
learning - Andrews Ng</a> lectures 2.1-2.7, 3.1-3.6, 4.1-4.6, 6.1-6.7, 7.1-7.4, <a
href="https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf" style="display: inline"
target="_blank"> Matrix cookbook</a>
</div>
<div class="kw">
<a href="https://forms.gle/LJCUPRHxHNAJzi8z7" style="display: inline"
target="_blank">Pre-Quiz</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Google_Colab_Notebooks/MachineLearning/L4"
style="display: inline" target="_blank">Notebook</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/MachineLearning/L4"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=2YyVqnGbkmQ" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L4"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L4/Solution"
style="display: inline" target="_blank">Solution</a> | <a
href="https://www.youtube.com/watch?v=SWuW_HtqRFI" style="display: inline"
target="_blank">Tutorial</a> | <a href="https://forms.gle/ehgPBBYtoyWvVfHp7"
style="display: inline" target="_blank">Quiz</a>
</div>
</div>
<div class="materials-item">
<div>
<b>Kernelization:</b> Dual form of an SVM, kernels for a dual form, examples of kernels and
their typical uses, SVR in primal form, SVR in dual form<br>
Prerequisites: Linear models, conditional probability, linear algebra <br>
Pre-work: <a href="https://www.youtube.com/watch?v=05VABNfa1ds" style="display: inline"
target="_blank">Support vector machine</a>, <a
href="https://www.youtube.com/watch?v=wBVSbVktLIY" style="display: inline"
target="_blank">Kernel trick</a>
</div>
<div class="kw">
<a href="https://forms.gle/3esw98p1JKgALSSz8" style="display: inline"
target="_blank">Pre-Quiz</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Google_Colab_Notebooks/MachineLearning/L5"
style="display: inline" target="_blank">Notebook</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/MachineLearning/L5"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=tL7WHI-fwYs" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L5"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L5/Solution"
style="display: inline" target="_blank">Solution</a> | <a
href="https://www.youtube.com/watch?v=LjKiHOaEL8g" style="display: inline"
target="_blank">Tutorial</a> | Quiz
</div>
</div>
<div class="materials-item">
<div>
<b>Feature selection and engineering:</b> T-test, forward selection, features for images,
features for audio, features for images, features for NLP,
PCA, ZCA, K-PCA<br>
Prerequisites: Basics of ML, linear algebra, mathematics <br>
Pre-work: <a
href="https://www.researchgate.net/profile/Samuel_Huang9/publication/275228384_Supervised_feature_selection_A_tutorial/links/5a1d720f0f7e9b2a531726a3/Supervised-feature-selection-A-tutorial.pdf"
style="display: inline" target="_blank">Supervised feature selection</a>, <a
href="https://www.machinelearningplus.com/machine-learning/feature-selection/"
style="display: inline" target="_blank">Feature Selection</a>, <a
href="http://openimaj.org/tutorial/pt02.html" style="display: inline" target="_blank">Image
fundamentals</a>, <a href="http://openimaj.org/tutorial/pt04.html" style="display: inline"
target="_blank">Audio fundamentals</a>, <a
href="https://towardsdatascience.com/extract-features-of-music-75a3f9bc265d"
style="display: inline" target="_blank">Music feature extraction in Python</a>
</div>
<div class="kw">
Pre-Quiz | Notebook | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/MachineLearning/L6"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=L0svV8rZvKM" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L6"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L6/Solution"
style="display: inline" target="_blank">Solution</a> | <a
href="https://www.youtube.com/watch?v=IVbTlIRj2Tk" style="display: inline"
target="_blank">Tutorial</a> | Quiz
</div>
</div>
<div class="materials-item">
<div>
<b>Dense and shallow neural networks:</b> Logistic regression as a sigmoid, single hidden layer
using sigmoid and ReLU, approximation of any function using a single hidden layer, overfitting,
advantage of multiple hidden layers, neural networks for regression, multi-regression,
multi-classification using softmax, back propagation.<br>
Prerequisites: Basics of ML, linear algebra, mathematics <br>
Pre-work: <a href="https://d2l.ai/chapter_multilayer-perceptrons/mlp.html"
style="display: inline" target="_blank">Multilayer Perceptrons</a>, <a
href="http://neuralnetworksanddeeplearning.com/chap4.html" style="display: inline"
target="_blank">Universal Approximation Theorem</a>, <a
href="https://d2l.ai/chapter_multilayer-perceptrons/backprop.html" style="display: inline"
target="_blank">Forward Propagation, Backward Propagation, and Computational Graphs</a>
</div>
<div class="kw">
Pre-Quiz | Notebook | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/MachineLearning/L7"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=hdNqyHEOm4w" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L7"
style="display: inline" target="_blank">Assignment</a> | Tutorial | Quiz
</div>
</div>
<div class="materials-item">
<div>
<b>Advanced topics in neural networks:</b> Weight initialization, momentum, weight decay, early
stopping, batch SGD, advanced optimizers such as RMSprop and ADAM<br>
Prerequisites: Basics of ML, neural networks, linear algebra, mathematics <br>
Pre-work: <a href="https://www.youtube.com/watch?v=wEoyxE0GP2M" style="display: inline"
target="_blank">Training neural networks I</a>, <a
href="https://www.youtube.com/watch?v=_JB0AO7QxSA" style="display: inline"
target="_blank">Training neural networks II</a>
</div>
<div class="kw">
<a href="https://forms.gle/sfiXdVPr71KYxfcB6" style="display: inline"
target="_blank">Pre-Quiz</a> | Notebook | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/MachineLearning/L8"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=oPgV4PMbI4A" style="display: inline"
target="_blank">Lecture</a> | Assignment | <a
href="https://www.youtube.com/watch?v=KdjBONblhHw" style="display: inline"
target="_blank">Tutorial</a> | Quiz
</div>
</div>
<div class="materials-item">
<div>
<b>Clustering:</b> K-means, DB-SCAN, agglomerative clustering, scaling of dimensions, goodness
of clustering<br>
Prerequisites: Euclidean distance, unsupervised learning, K-means <br>
Pre-work: <a href="https://www.youtube.com/watch?v=Ev8YbxPu_bQ" style="display: inline"
target="_blank">Introduction to clustering</a>, <a
href="https://www.youtube.com/watch?v=qg_M37WGKG8" style="display: inline"
target="_blank">K-means clustering</a>, <a
href="https://en.wikipedia.org/wiki/Cluster_analysis" style="display: inline"
target="_blank">Cluster analysis</a>
</div>
<div class="kw">
<a href="https://forms.gle/QtcsX6u3xnKDgyUN9" style="display: inline"
target="_blank">Pre-Quiz</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Google_Colab_Notebooks/MachineLearning/L9"
style="display: inline" target="_blank">Notebook</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Slides/MachineLearning/L9"
style="display: inline" target="_blank">Slides</a> | <a
href="https://www.youtube.com/watch?v=co9XJids5SM" style="display: inline"
target="_blank">Lecture</a> | <a
href="https://github.com/shala2020/shala2020.github.io/tree/master/Lecture_Materials/Assignments/MachineLearning/L9"
style="display: inline" target="_blank">Assignment</a> | <a
href="https://www.youtube.com/watch?v=fdYCCP8F7X0" style="display: inline"
target="_blank">Tutorial</a> | Quiz
</div>
</div>
<div class="module-header" id="break">Interim break (May 18 - May 24)</div>
<p>Classes will resume from May 25 with a new schedule. This week, please catch up with the recorded
<a href="https://www.youtube.com/channel/UCobe_Yc7nV6kux94A5doIiA/" target="_blank">lectures</a>,
assignments, and the tutorials on <a href="https://www.tensorflow.org/"
target="_blank">TensorFlow</a> as given below:
</p>
<div class="kw">
<a href="https://www.tensorflow.org/tutorials" style="display: inline" target="_blank">TensorFlow
tutorials</a> |
<a href="https://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf" style="display: inline"
target="_blank">CS224d - TensorFlow Tutorial</a> |
<a href="https://github.com/aymericdamien/TensorFlow-Examples" style="display: inline"
target="_blank">TensorFlow Examples</a> |
<a href="https://machinelearningmastery.com/tensorflow-tutorial-deep-learning-with-tf-keras/"
style="display: inline" target="_blank">Deep Learning With tf.keras</a>
</div><br>
<p>If you are all caught up with the material covered so far, then please try some ML problems such as
those given below:</p>
<div class="kw">
<a href="https://www.analyticsvidhya.com/blog/2018/05/24-ultimate-data-science-projects-to-boost-your-knowledge-and-skills/"
style="display: inline" target="_blank">Machine Learning projects</a> |
<a href="https://data-flair.training/blogs/data-science-project-ideas/" style="display: inline"
target="_blank">Data Science projects-1</a> |
<a href="https://www.edureka.co/blog/data-science-projects/" style="display: inline"
target="_blank">Data Science projects-2</a>
</div>
</div>
</div>
</div>
<div class="sechighlight">
<div class="container sec">
<h2 id="prerequisites">PRE-REQUISITES</h2>
<div id="coursedesc">
<p> <b>Computer and connectivity:</b> 8GB+ RAM, 20GB of free disk space, 100kbps+ connectivity
</p>
<p><b>Knowledge:</b> This course is directed at engineering students. Others who know the following
topics
are also
welcome: Linear algebra (vectors and matrix arithmetic, projection of vectors, singular value
decomposition), calculus (differentiation, partial derivatives, double derivatives, chain rule of
derivatives), programming (preferably python, loops, conditions, functions, classes, programming
best
practices such as modularity, commenting, and informative variable naming).</p>
<p><b>Status:</b> Student with a valid college ID card. Not restricted to India.
</p>
<p><b>Study Materials</b>: Please go through the following before April 15:
<ol>
<li>Basic mathematics for machine learning (linear algebra, calculus, probability and
statistics), such
as <a href="https://gwthomas.github.io/docs/math4ml.pdf" target="_blank">Mathematics for
Machine Learning</a>. It will be better if you actually do all the math by
hand on paper and pencil while following the material. Just reading it is not enough
for retention
of concepts.</li>
<li>Basics of Python programming such as <a href="https://www.learnpython.org/"
target="_blank">Learn Python</a>. There are many other good ones
too.</li>
</ol>
</p>
</div>
</div>
</div>
<!-- <div class="container sec"> -->
<!-- Introducing stfd template -->
<div class="container sec">
<h2 id="team">TEAM</h2>
<div class="row">
<div class="col-md-12">
<div style="margin-bottom: 10px">
<h2>Instructors</h2>
</div>
<div class="instructor">
<a href="https://in.linkedin.com/in/abhijeet-patil-90945557" target="_blank">
<div class="instructorphoto"><img src="images/ab.jpeg"></div>
<div>Abhijeet Patil</div>
</a>
<div>PhD*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.ee.iitb.ac.in/~asethi/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/as.jpg">
</div>
<div>Amit Sethi</div>
</a>
<div>Professor, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/ankur-deka-120392135" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/ad.jfif">
</div>
<div>Ankur Deka</div>
</a>
<div>MS*, CMU</div>
<br>
</div>
<div class="instructor">
<a href="http://arjunjain.co.in/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/aj.jfif">
</div>
<div>Arjun Jain</div>
</a>
<div>Professor, IISc</div>
<br>
</div>
<div class="instructor">
<a href=" https://www.linkedin.com/in/dipteshkanojia/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/dk.jfif">
</div>
<div>Diptesh Kanojia</div>
</a>
<div>PhD*, IITB</div>
<br>
</div>
<div class="instructor">
<a href=" https://www.linkedin.com/in/hiteshkurapati/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/hk.jpeg">
</div>
<div>Hitesh Kurapati</div>
</a>
<div>MTech*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/jyotikrishnadass" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/jd.jfif">
</div>
<div>Jyotikrishna</div>
</a>
<div>PhD*, TAMU</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/kaveri-kale-37138242/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/kaveri.jpg">
</div>
<div>Kaveri Kale</div>
</a>
<div>PhD*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/malaikannan" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/mk.jpeg">
</div>
<div>Malaikannan</div>
</a>
<div>VP, Saama Tech</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/mankhandelwal/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/manishK.jpeg">
</div>
<div>Manish Kumar</div>
</a>
<div>Sr.Eng, DENSO </div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/ipsit-mantri-b61945170" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/im.jfif">
</div>
<div>Mantri Ipsit</div>
</a>
<div>BTech*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/nikhil-cherian-kurian-7bba285b/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/nc.jfif">
</div>
<div>Nikhil Kurian</div>
</a>
<div>PhD*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/parth-dodhia-b61a831a4" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/profile-pic.png">
</div>
<div>Parth Dodhia</div>
</a>
<div>BTech*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/pseemakurthi" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/ps.jfif">
</div>
<!-- <div>Prasad Seemakurthi</div> -->
<div>Prasad</div>
</a>
<div>Mentor, Grab</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/sharmakprashant/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/pk.jfif">
</div>
<div>Prashant</div>
</a>
<div>MTech*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/ravinderkhatri/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/rs.jpeg">
</div>
<div>Ravinder Singh</div>
</a>
<div>DS, Xebia</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/snshamsi/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/ss.jfif">
</div>
<div>Saqib Shamsi</div>
</a>
<div>Engr, Whirlpool</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/sayan-chatterjee-792918171" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/sc.jfif">
</div>
<div>Sayan Chatterjee</div>
</a>
<div>MTech*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/shreyans-dhankhar-501b88118" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/sd.jfif">
</div>
<div>Shreyans</div>
</a>
<!-- <div>Data Scientist, HP</div> -->
<div>DS, HP</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/sudhakarkumar2" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/sk.jfif">
</div>
<div>Sudhakar Kumar</div>
</a>
<div>MTech*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/sumandeep-banerjee-1436a17" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/sb.jfif">
</div>
<div>Sumandeep</div>
</a>
<div>ML Engineer</div>
<br>
</div>
</div>
</div>
<!-- Orgarnizers List -->
<div class="row">
<div class="col-md-12">
<div style="margin-bottom: 10px">
<h2>Organizers</h2>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/ayuc140/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/aac.jfif">
</div>
<div>Aayush</div>
</a>
<div>Analyst, Toppr</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/aayushk614/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/ak.jfif">
</div>
<div>Aayush Kumar</div>
</a>
<div>Intern, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/abhishek-chikane-baa03a196" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/ac.jfif">
</div>
<div>Abhishek</div>
</a>
<div>RA, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/akshaybajpai31" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/ab.jfif">
</div>
<div>Akshay Bajpai</div>
</a>
<div>MTech*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/profile-pic.png">
</div>
<div>Deep</div>
</a>
<div>Volunteer</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/mohit-agarwala-76a48ab8/" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/ma.jfif">
</div>
<div>Mohit Agarwala</div>
</a>
<div>MTech*, IITB</div>
<br>
</div>
<div class="instructor">
<a href="https://www.linkedin.com/in/mukularoradce" target="_blank">
<div class="instructorphoto">
<img class="img-hover" src="images/profile-pic.png">