Machine Learning And Deep Learning - Fundamentals And Applications [Introduction Video]
Summary
TLDRDr. MK Bhuya introduces a comprehensive course on machine learning and deep learning at IIT Guwahati. The course aims to familiarize students with the fundamentals, including statistical and soft computing techniques, and their applications. It covers a range of topics from theory and algorithms to practical implementations in areas like biometrics and computational biology. The course is structured over 12 weeks, touching on supervised and unsupervised learning, neural networks, and deep learning architectures. Mathematical concepts like linear algebra and probability are emphasized, and the course is complemented by recommended textbooks and a course website.
Takeaways
- 🎓 This course is led by Dr. MK Bhuya, a professor from the Department of Electronics and Electrical Engineering at IIT Guwahati.
- 🌟 The course aims to familiarize students with the broad areas of machine learning and deep learning, focusing on both theoretical and practical aspects.
- 🧠 Machine learning is defined as a subset of artificial intelligence where algorithms learn from data without explicit programming.
- 📚 The course covers statistical machine learning, soft computing-based techniques, and artificial neural networks.
- 🔢 Mathematical concepts, particularly linear algebra and probability, are crucial for understanding machine learning and deep learning.
- 📈 The course differentiates between traditional programming and machine learning, highlighting the latter's ability to generate programs from data and desired outputs.
- 📊 Types of learning include supervised, unsupervised, semi-supervised, and reinforcement learning, each with specific applications and data requirements.
- 🌐 Machine learning and deep learning have wide-ranging applications in areas such as finance, healthcare, robotics, and social media.
- 📈 The course is structured into 12 weeks, covering topics from introduction to machine learning to recent trends in deep learning architectures.
- 📚 Recommended books for the course include 'Pattern Classification' by Duda, Hart, and Stork, 'Pattern Recognition and Machine Learning' by Bishop, and 'Deep Learning' by Goodfellow et al.
- 💡 The course emphasizes the importance of understanding mathematical concepts such as vectors, dot products, eigenvalues, and probability distributions for grasping machine learning and deep learning concepts.
Q & A
Who is the professor teaching the course on machine learning and deep learning?
-Dr. MK Bhuya, a professor from the Department of Electronics and Electrical Engineering at IIT Guwahati.
What is the primary goal of the course on machine learning and deep learning?
-The primary goal is to acquaint students with the broad areas of machine learning and deep learning, focusing on fundamental concepts, theories, principles, and algorithms.
What mathematical concepts are important for understanding machine learning and deep learning according to the course?
-Understanding mathematical concepts such as linear algebra, probability, and random processes is quite important for grasping the concepts of machine learning and deep learning.
What is the definition of machine learning as per the course?
-Machine learning is defined as a subset of artificial intelligence where algorithms have the ability to learn without being explicitly programmed.
How is deep learning related to machine learning?
-Deep learning is a subset of machine learning where artificial neural networks adapt and learn from large amounts of training data.
What are the different types of learning methods discussed in the course?
-The course discusses supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
What is the fundamental distinction between traditional programming and machine learning?
-In traditional programming, the input is data and the program produces the output. In machine learning, the input is data and the output, and the goal is to generate the program or algorithm.
What are some applications of machine learning and deep learning mentioned in the course?
-Applications include species recognition, speech pattern recognition, biometrics, web search, computational biology, finance, e-commerce, space exploration, robotics, and more.
What programming environments are recommended for the course?
-The course suggests considering OpenCV, Python, and Matlab as programming environments.
What are some of the key topics covered in the course?
-Key topics include Bayesian classification, linear regression, maximum likelihood estimation, perceptron, support vector machines, decision trees, random forests, and deep learning architectures like CNNs, RNNs, and autoencoders.
What are the recommended textbooks for the course?
-Recommended textbooks include 'Pattern Classification' by Duda, Hart, and Stork, 'Pattern Recognition and Machine Learning' by Bishop, and 'Computer Vision and Image Processing: Fundamentals and Applications' by MK Bhuya.
Outlines
🎓 Introduction to Machine Learning and Deep Learning Course
Dr. MK Bhuya, a professor from the Department of Electronics and Electrical Engineering at IIT Guwahati, introduces a course on machine learning and deep learning. The course aims to familiarize students with the fundamental concepts and applications of these fields. It will cover statistical machine learning, soft computing-based techniques, and the importance of mathematical understanding for grasping these subjects. The course outline includes discussions on artificial intelligence, machine learning, and deep learning definitions, distinctions between traditional programming and machine learning, and types of learning such as supervised, unsupervised, semi-supervised, and reinforcement learning. Applications in various fields like biometrics, web search, and finance are also highlighted.
📚 Course Prerequisites and Week-wise Content Distribution
The course prerequisites include motivation, basic coordinate geometry, matrix algebra, linear algebra, probability, and random processes, along with programming skills in open source software like Python or MATLAB. The course content is distributed week-wise, starting with an introduction to machine learning, performance measures, and linear regression. Subsequent weeks delve into Bayesian decision theory, density estimation, perceptron criteria, logistic regression, support vector machines, decision trees, hidden Markov models, ensemble methods, dimensionality reduction techniques like PCA and LDA, and clustering techniques. The course also covers mixer models, neural networks, and deep learning architectures, concluding with recent trends in deep learning.
📘 Recommended Textbooks and Course Structure
Dr. Bhuya recommends several textbooks for the course, including 'Pattern Classification' by Duda, Hart, and Stork, 'Pattern Recognition and Machine Learning' by Bishop, and his own book 'Computer Vision and Image Processing: Fundamentals and Applications'. The course is divided into three parts: supervised machine learning techniques, unsupervised machine learning techniques, and artificial neural networks and deep learning architectures. Each part covers a range of topics from linear regression to advanced deep learning models like convolutional neural networks and autoencoders. The importance of understanding mathematical concepts such as linear algebra and probability is emphasized for the successful completion of the course.
Mindmap
Keywords
💡Machine Learning
💡Deep Learning
💡Artificial Neural Networks
💡Supervised Learning
💡Unsupervised Learning
💡Reinforcement Learning
💡Statistical Machine Learning
💡Soft Computing
💡Pattern Recognition
💡Dimensionality Reduction
💡Convolutional Neural Networks (CNNs)
Highlights
Introduction to the course on machine learning and deep learning by Dr. MK Bhuya, a professor at IIT Guwahati.
The course aims to familiarize students with the broad areas of machine learning and deep learning.
Machine learning is defined as a subset of artificial intelligence with algorithms that learn from examples.
Deep learning is presented as an advanced version of artificial neural networks that learn from large datasets.
The course will cover statistical machine learning and soft computing-based techniques like artificial neural networks and fuzzy logic.
Mathematical concepts, particularly linear algebra and probability, are emphasized as crucial for understanding machine learning.
The course outline includes topics like artificial intelligence, machine learning, deep learning, and their distinctions.
Different types of learning in machine learning are discussed, including supervised, unsupervised, semi-supervised, and reinforcement learning.
Applications of machine learning and deep learning are explored, such as in finance, healthcare, and robotics.
The course prerequisites include a strong foundation in coordinate geometry, matrix algebra, linear algebra, probability, and programming.
Programming languages like Python and MATLAB are recommended for the course.
The course will delve into fundamental machine learning concepts like Bayesian classification and linear regression.
Machine learning techniques such as decision trees, random forests, and support vector machines will be covered.
The course will also discuss dimensionality reduction techniques like PCA and linear discriminant analysis.
Unsupervised techniques including clustering methods like K-means and mean shift will be part of the curriculum.
Deep learning architectures like convolutional neural networks, LSTM, VGG, and GoogleNet will be introduced.
The course will conclude with recent trends in deep learning, transfer learning, and autoencoders.
Recommended books for the course include 'Pattern Classification' by Duda, Hart, and 'Pattern Recognition and Machine Learning' by Bishop.
The course is divided into three parts: supervised machine learning techniques, unsupervised machine learning techniques, and artificial neural networks and deep learning architectures.
Transcripts
foreign
[Music]
course on machine learning and deep
learning fundamentals and applications I
am Dr MK bhuya professor of the
Department of electronics and electrical
engineering IIT guwahati this is a
course on machine learning and deep
learning so I'll be discussing some
fundamental concepts of machine learning
and deep learning and also some
applications the objective of this
course is to acquaint students with the
broad areas of machine learning and deep
learning machine learning is an exciting
research area the goal is to design
machines that can learn from the
examples in this course I will focus on
Theory
principles and some algorithms of
machine learning and deep learning and
in case of the machine learning mainly I
will consider the statistical machine
learnings and also the soft Computing
based machine learning techniques also
understanding of mathematical Concepts
is quite important to understand the
concept of machine learning and deep
learning let me briefly explain the
course outline the course on machine
learning and deep learning so here you
can see I have shown the what is
artificial intelligence what is machine
learning and what is deep learning
artificial intelligence is nothing but
the programs with the ability to learn
and reasons like human so this is one
Definition of artificial intelligence
machine learning is a subset of
artificial intelligence algorithms with
the ability to learn without being
explicitly programmed so that is the
definition of machine learning
in this course mainly I will focus the
concept of statistical machine learning
and also the soap Computing based
machine learning techniques in the soap
Computing base machine learning
techniques I will be discussing the
artificial neural networks and also the
fuzzy logic and here you can see the
Deep learning is a subset of machine
learning
in which artificial neural networks
adapt and learn from huge amount of
training data so that means it is the
advanced version of artificial neural
networks next you can see I am showing
the distinction between the traditional
programming and the Machine learning in
case of the traditional programming
input to the computer is data in the
program and corresponding to this I am
getting the outputs
So based on my programs and based on my
data I will be getting the output
but in case of the machine learning I
know what is the output
so input to the system is data and
output and from this I have to generate
the program so I have to write the
program or I have to develop the
algorithm so you can see the fundamental
distinction between traditional
programming and the Machine learning and
the types of learning in case of the
machine learning or deep learning so one
is the supervised learning in this case
we have training data and also we know
what is the desired outputs in case of
the unsupervised learning we have
training data but we do not know what
are the desired outputs in case of the
semi-supervised learning we have
training data and a few desired outputs
so generally ah semi supervised learning
techniques are used in medical image
analysis because it is very difficult to
get the level data so this technique is
used in some of the applications of
medical image analysis and finally
reinforcement learning that is the
rewards from sequence of action that
means the goal is more important rather
than a single action
so that means the group of actions are
more important rather than a single
action
corresponding to a good action a reward
will be given and this is the
fundamental definition of the
reinforcement learning so in case of the
machine learning or in case of a deep
learning we have to build a machine
that can recognize patterns and some of
the examples like this ah the species
recognition the speech is a pattern the
speech signal is a pattern
in case of the Biometrics fingerprint
identification face recognition and some
other applications like optical
character recognition DNA sequence
identifications biomedical image
analysis biomedical signal analysis
digit recognition molecular
classifications so there are many
applications so now I'm up to the next
slide so some of the applications like
web search computational biology Finance
e-commerce space Explorations robotics
information extraction social networks
debugging software there are many other
applications so this application I can
show like this
you can see in this case I am showing
the applications of machine learning and
deep learning
so one is the finance one is gaming
astronomy Healthcare transport
agriculture education e-commerce
entertainment robotics Automotive social
media and data security so there are
many applications of machine learning
and deep learning so overview of this
course so ah let me introduce this
course
so PR requisites
strong motivation basic coordinate
geometry Matrix algebra linear algebra
and probability and random process and
for programming you may consider opencp
python that is very popular programming
environment or also in many cases you
can consider Matlab so background
already I told you mainly you have to
know about the linear algebra and the
probability mainly the concept of
vectors
dot products eigenvectors and eigen
values and you can see appendix of
different textbooks for all these
mathematical Concepts particularly the
linear algebra and the probability
the concepts like mean variance normal
distributions so all these you can get
in the books in this course you can see
I will be discussing some fundamental
concepts of machine learning and the
Deep learning like the Bayesian
classification the concept of univariate
and multivariate normal densities linear
regression maximum likelihood estimation
name based classification perceptron and
basic single layer neural networks
linear discrimination and gradient
descent optimization logistic regression
support Vector machine
regularized risk minimization decision
trees random forest and the concept of
Ensemble classification like begging and
the boosting pressure selection and one
important concept is dimensionality
reduction by considering PCA and the
linear discriminate analysis
clustering and also the another
important concept is hidden markup
models and that deep learning
so this is the outline of the course so
our Focus will be on applying machine
learning to real applications so week
wise distribution of this course
will be like this
so in the first week I will be
discussing the introduction of machine
learning
performance measures
and the linear regression so I may take
three classes for this week
next one is in the second week I will be
discussing the concept of Bayesian
decision Theory normal density and the
discriminate functions Bayesian decision
Theory binary features and one important
concept is the belief Network
so I may take five classes for this week
in the third week I will be discussing
two important Concepts one is the
parametric and the non-parametric
density estimation
and mainly I will be considering the
maximum likelihood estimation and the
Bayesian estimation for the parametric
estimation
and for the non parametric estimation I
will be discussing the concept of the
Persian window and the K nearest
neighbor technique so I may take 4
classes for this week in the week number
four I will be discussing the concept of
perceptron criteria logistic regression
and also I will be discussing
discriminative models and one
discriminative model is the support
Vector machine so that concept I will be
discussing in the week number four after
this in the week number five two
important Concepts one is the decision
trees another one is the concept of the
Hidden Miracle model hidden Markov model
is a graphical model which is used to
predict a set of variables promise setup
observed variables and week number six I
will be considering the concept of The
Ensemble methods
so the concepts like boosting and
begging and one important concept is the
random forest in week number seven I
will be discussing one important concept
that is the concept of dimensionality
problem
so to reduce the dimension of a pixel
Vector I will be discussing the concept
of PCA the principal component analysis
and the linear discriminant analysis in
week number eight another concept that
is the concept of mixer model
I will be discussing
and for this I will be discussing the
concept of gaussian mixer model
and for estimating the parameters I will
be discussing the concept of expectation
maximization algorithm so for this week
I may take two classes in in the week
number nine I will be discussing
unsupervised techniques
like the clustering K means clustering
like mean shift clustering so all these
clustering techniques I will be
discussing in week number nine in week
number 10 I'll be discussing the
fundamental concepts of neural network
artificial neural network perceptron
multi-layer neural networks the concept
of the back propagation RBF neural
networks and some applications of the
neural networks and in this case I will
be discussing both supervised and the
unsupervised neural networks in the week
number 11 I will be introducing the
fundamental concept of deep neural
networks and mainly I will be discussing
the concept of convolution ah neural
networks LX net vgg net and the Google
net and finally in the week number 12 I
will be discussing recent Trends in deep
learning architectures the concept of
transfer learning residual networks the
concept of Auto encoders and its
relation to the PCA the principal
component analysis and there are many
other applications of the deep neural
networks so all these things I will be
discussing in the week number 12. so
this is the week wise distribution of
the course
regarding the books the first book you
can consider that is the Alpine
introduction to machine learning so that
book also you can see and most of the
topics I will be following from the book
by to die and hurt the second book so
this is the book name is pattern
classification and this is a very
important book and another book is by
Bishop pattern recognition and machine
learning that book also you can see for
some of the concepts so another book my
book is MK bhuya
and the name of the book is computer
vision and image processing fundamentals
and applications published by CRC place
so for some of the important Concepts I
will be following this book and for the
Deep learning you can follow a book by
good pillow
so this is about the books in the course
website also you can see
the name of these books so here you can
see I am dividing the entire course into
three parts
the first part is the supervised machine
learning techniques the second part is
unsupervised machine learning techniques
and finally in the third part I am
considering artificial neural networks
and the Deep learning architectures
in case of the supervised learning
techniques first I will introduce the
concept propagation linear regression
after this the Bayesian decision Theory
and in this case I will be discussing
some estimation techniques the parameter
estimation technique
like maximum likelihood estimation and
the Bayesian estimation
and also I will be discussing non
parametric techniques for this I will be
discussing two important Concepts the
Persian window technique and the K
nearest neighbor techniques
this is about the generative models in
case of the discriminative models I will
be discussing one important algorithm
that is the support Vector machine
and some of the other topics like
decision trees hidden Markov model so
all these Concepts I will be discussing
here also I will be discussing some
Ensemble based learnings like the
concept of begging boosting and concept
of the adoboost classifier and also the
concept of the random forest in part
number two I will be discussing
unsupervised machine learning techniques
mainly I will be considering the concept
of the k-means clustering fuzzy Siemens
clustering
ah the mean ships clustering so all
these clustering techniques I will be
discussing in part number two
and finally in the part number three I
will be discussing the concept of the
supervised and the unsupervised
artificial neural networks
and also the concept of Deep
architectures
so this is the course outline
of this course on machine learning and
deep learning fundamentals and
applications
and one important point is the
understanding of the mathematical
Concepts like the understanding of the
linear algebra and the probability and
the random process
so I hope you will enjoy this course
thank you
[Music]
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