Machine Learning And Deep Learning - Fundamentals And Applications [Introduction Video]

Summary

Dr. 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

00:00

🎓 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.

05:02

📚 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.

10:03

📘 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.

Keywords

💡Machine Learning

Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from data, identify patterns, and make decisions with minimal human intervention. In the context of the video, Dr. MK Bhuya emphasizes the importance of machine learning as a field that aims to design machines capable of learning from examples, which is central to the course's objective of familiarizing students with its broad areas and applications.

💡Deep Learning

Deep learning is a subset of machine learning that deals with artificial neural networks and their ability to learn from and make decisions based on large amounts of data. The video script discusses deep learning as an advanced version of artificial neural networks, highlighting its role in adapting and learning from extensive training data sets, which is a fundamental concept in the course's exploration of machine learning techniques.

💡Artificial Neural Networks

Artificial neural networks are computational models inspired by the human brain that are used to recognize patterns and solve complex problems. In the script, Dr. MK Bhuya mentions artificial neural networks as a key component of both machine learning and deep learning, where they are used to model complex relationships and perform tasks such as image and speech recognition.

💡Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data, meaning the training data includes both the input and the desired output. The video script explains supervised learning as one of the types of learning in machine learning, where the system is provided with training data and the corresponding desired outputs, which is crucial for tasks like classification and regression.

💡Unsupervised Learning

Unsupervised learning is a type of machine learning where the model is trained on data without labeled responses, allowing the model to discover patterns and relationships within the data. In the video, unsupervised learning is mentioned as a method where training data is provided without desired outputs, which is useful for tasks like clustering and association.

💡Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. The script introduces reinforcement learning as a method focused on rewards from sequences of actions, emphasizing the importance of the overall goal over single actions, which is a significant aspect of the course's exploration of learning techniques.

💡Statistical Machine Learning

Statistical machine learning involves the use of statistical techniques to build machine learning models. The video script discusses statistical machine learning as a primary focus of the course, where Dr. MK Bhuya will cover algorithms and techniques that rely on statistical methods to make predictions and decisions.

💡Soft Computing

Soft computing encompasses computational techniques that are tolerant of imprecision, uncertainty, and partial truth, such as fuzzy logic and neural networks. In the script, soft computing is mentioned as a basis for machine learning techniques, including artificial neural networks and fuzzy logic, which are used to handle the complexity and ambiguity in data.

💡Pattern Recognition

Pattern recognition is the ability of a system to identify and classify patterns in data, which is a fundamental aspect of machine learning. The video script highlights pattern recognition as a key application of machine learning, with examples such as species recognition, speech recognition, and biometric identification, showcasing the practical utility of the techniques discussed in the course.

💡Dimensionality Reduction

Dimensionality reduction is the process of reducing the number of variables under consideration, which can help improve the performance of machine learning models. The script mentions dimensionality reduction techniques like PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) as important concepts in the course, which are used to simplify data while retaining essential information.

💡Convolutional Neural Networks (CNNs)

Convolutional neural networks are a class of deep neural networks widely used for processing grid-like data, such as images. The video script introduces CNNs as a fundamental concept in deep learning, discussing architectures like AlexNet, VGGNet, and GoogleNet, which are crucial for understanding the course's exploration of deep learning applications in image and video analysis.

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]