Machine Learning

Welcome to machine learning for engineering and science applications This is the first video for the course so in this course we will be looking at in this video we will be

looking at the introduction to the course and a brief history of artificial intelligence

through the ages So let us look at a few things that we are using that are essentially products

of machine learning in real life today We have seen Uhh Amazon's recommendation system You would have had several such things at

using other software or other websites also Basically you buy a bunch of stuff of this website and it recommends other things you know maybe books other books or other product

that you might like This is Amazon's Echo it is effectively run by a speech recognition

engine combined with website searches We have been all using Google spam filter or any other

company spam filter but Google works really really well as a part of our mail system it

works very seamlessly nowadays This is Google's Lexus which is a self driving car So now what is common between all these

all these 4 is essentially all of them use as an essential part of their algorithm machine

learning algorithms Okay So machine learning very simplistically speaking is a method or

a set of algorithms that you can use to replicate activities typically that require human cognition

So for speech humans recognise speech very very well starting from a baby to another we do it very quickly We all drive those of us that drive drive fairly seamlessly spam

filter most of us can look at email and almost instantaneously say whether we spam or not

However in practice in order to encode it into an algorithm is actually a difficult task Because a number of rules expands very very rapidly you cannot say that a mail if

it comes from Nigeria is definitely going to be spam if it involves money it is going

to be spam etc etc So there is a finite set of rules that you can make but nonetheless

you would like a quick spam filter which works as well as a human being does So in such circumstances

we tend to use a large basket of techniques these are called Machine learning models these are very old models several of them are at least half a century or even sometimes even

a century old So we will be looking at many of these algorithms but as far as this course is concerned this

course the course of machine learning can be part of electrical injury it can be part of computer science it is a branch of mathematics We are going to treat it as if it is going

to be primarily used for engineering and science applications Just to give you a couple of applications in mind When you have an x-ray or you have an MRI that a radiologist looks

like looks at and stars giving you a diagnosis okay like the other tumour here this is cancerous

not cancerous etc etc Can we replicate this sort of process using machine learning algorithm Just like we are

able to replicate driving can we replicate this kind of judgement Doctor Ganapathy for

example is an expert in this field Another application could be we will see this in the

middle of this course that this is actually flow past a cylinder So what we are trying

to predict is not this thing is if you have a circular body cylinder in this case kept

within an external flow you know it starts giving what you see here are velocity contours Traditionally even today we use several software and the whole process is called computational

fluid dynamics often abbreviated as CFD You might use solid mechanics modelling or any

continuum based PDE-based modelling the question is can we find out models that can do this

more rapidly using machine learning And we will see that indeed a part of it is possible and this is an exciting field So our aim in this course is the following

First thing is to want to understand basic machine learning models thoroughly In specific

we are going to look at what is now very popular Deep Learning we will see what it is even

later on today Machine learning models thoroughly and in particular some very fundamental models

that have been used for almost 50 years now at various stages of development We will also

look at some modern machine learning techniques which have caught on as the last of the last

decade or sometimes even as recently as last few years We will be looking at several things that have been done in this field even in the last

year the field is moving very very rapidly We located in the context of engineering applications

mostly And finally what we want to do is to apply these techniques hands on to problems

in engineering Now within a video course of the sort that we are taking right now there is only so much that we can do We will look at some basic coding paradigms we will also

show you some examples but the hope is yourself motivated and you do learn and program in

Python by yourself We will give you the basic rudiments and will give you several basic examples you know for

example from medical image diagnosis from turbulence modelling CFD competition etc But

our expectation is that you will also do some work on your own okay whether you the course for credit or not you can get the maximum out of this course in case you tend to code

yourself So we also hope that at the end of this course you should be able to read and

understand research papers in machine learning and especially applied research papers You might not be able to understand you know very hard-core machine learning theory but

if somebody has applied machine learning to some practical problem hopefully you be able to read a paper and understand it This is also a primary aim of the course because most

of the development that is happening today is not necessarily present in textbooks etc

It is mostly available as research papers especially an archive So our hope is you should

be able to get this also so this is a broad course aim okay So it intersects with other courses from machine learning but the emphasis is a little bit

more on the application side And getting an overview and getting basic idea of the models

that are in play Okay So in terms of prerequisites for the course what is it that you require

in order make sure that you can complete the course successfully One of the primary requirements

that you would have is mathematical sophistication This sophistication is you know a vague term

what it means is that you are comfortable with mathematics thinking about things in a mathematical framework

Rather than just thinking of it in a vague framework which is just qualitative we would like you to have a quantitative mindset While we will be introducing ideas from you know

whatever our essential ideas you need not the whole of linear algebra or property or optimisation for that matter But we will be introducing you to some basic ideas that are

required for the course okay Nonetheless that portion will be one week each but more importantly you must be comfortable

okay whenever we talk about something in mathematical terms especially when it comes to probability

you should be comfortable in thinking through these things in a mathematical framework that is one Similarly slightly similar in this respect is you should be comfortable with

programming Hopefully you have written programs at least in some language we will be using

examples from Python examples from MATLAB So we expect you to be able to at least understand

syntax from Python and MATLAB But you will get the maximum out of the course if you are actually comfortable with programming

itself and you can do some hands-on exercises We will be giving suggested exercises throughout this course so hopefully you should be able to do these especially if you are trying to

take a course for credit So here is an outline of the course So we will have 3 broad parts of the course the

first is artificial neural networks and Deep learning this includes what are called CNNs

CNNs are convolutional neural networks these are used for vision RNNs are used for typically

sequential data So we will be using RNNs CNNs as well as what are typically called Simple

ANNs and this is the first part of the course The 2nd part of the course is other classical techniques that are being used for a long time

They are still applied in various areas depending on the complexity of the problem Tree-based methods support vector machines probabilistic methods etc this is the 2nd part of the course

Finally we look at some modern techniques such as derivative adversarial networks etc

and reinforcement learning if time permits As far as applications are concerned within

each module we will discuss various applications for each of those modules as we go for during

the course So here is the syllabus for the course this was announced on the website also So the first

3 weeks essentially are the basics okay So all the fundamentals that are required for

the course For the first week we are going to look at linear algebra primarily then the

2nd week will be probability and statistics whatever basics are required visited the whole course in itself not just whatever basics are required for this course The 3rd week

would be whatever numerical computation and optimisation basics you require And also popular

machine learning packages that are available today we look at an overview of those Week 4 and 5 is essentially neural networks So it is possible to think of even linear

regression are very very simplified neural networks So we look at linear regression and logistical regression these are 2 basic algorithms the simplified algorithm then complex neural

networks and multilayer neural networks in the next couple of weeks The next 3 weeks

are essentially variations on neural networks So this involves convolutional neural networks

this is for vision Vision-based problems are usually solved using convolutional neural

networks Recurrent neural networks are typically used for sequence-based problems okay So sequence

that develop in time for example So timeseries analysis in some sense can be used using recurrent

neural networks Then we look at classical techniques techniques that have been around for a long time and they are still used in conjunction with Deep learning and neural

networks Some probabilistic techniques also we will be covering for example Gaussian mixture

models etc Okay Unsupervised learning will also be covered here finally we will look at some advanced

techniques there might be some changes here as we go forth in the course depending on how students are doing We can also add a reinforcement learning if time permits towards the end of

this The reference books for this course The first is by now even though this is published only

in 2016 it is already treated as with is a classic text It is a very good text called Deep Learning by MIT press Goodfellow and Bengio etc are all researchers in the field

The 2nd is Pattern Recognition and Machine Learning this is also a very very good text though it is a dense text it is a little bit harder to read but it is an exceptionally

well-written text very very thorough text by Christopher Bishop And the 3rd is towards the practical implementation side Uhh of deep learning this is by Francois

Cholle it is called Deep Learning with Python Now fortunately the first 2 texts are actually

available for free Okay These have been made available by the publishers themselves this

is legal you can search for these texts and you will find websites where these books are

being checked by the publishers themselves I would very highly recommend that you go forth and take a look at it and also read through these texts as the course progresses

Please use the resources that have been generously made available by the publishers for those

we are really grateful for them to having done that So now let us look at the history

of artificial intelligence through the ages Idea of artificial intelligence has been around

for a really really long time It is almost as old as Uhh you know whenever we started

making tools Why is it we are covering it one to see that many of the ideas that we are covering are actually quite old also to see the ebb and flow of the ideas when the

ideas go up and when they come down and some of the ideas we might be covering in this course right now quite suddenly become unpopular for 5-10 years but suddenly might become popular

again So it is a good idea to know where a topic comes from So as I said history of artificial

intelligence is really really old Humankind has been fascinated with tools they have been

fascinated with what we can do by looking at our hands we have started looking at you know what kind of machine tools can I make so that I can replicate the motion of my hand

So similarly just like we have been thinking about mechanical tools we have also been thinking about thinking tools

So can I not only replicate whatever I am motions I am making the wheel for transportation

the hand for working the lever for lifting etc etc not just that but can we actually

make tools that can ease our thinking This is Raju Bhoj of Parmar this is from Bhopal

So this is speculation I do not want to say that he actually made a machine he did not

So within his works rather bought was very very accomplished person a poet an engineer

he has a fantastic civil engineering work all sorts of things He also speculated that you could have proposed that imitated or replicated human speech and

motion And even before him and after him there have been several people throughout the world increase in Rome etc who have been doing this There were realistic automatons in several

parts of the world you know right from prehistory till date One such example though it is a

fake example is what is called the Mechanical Turk This was claimed by essentially a con

man that he could make a he could make a machine that could play chess

What he actually had was a person inside a person who could play chess inside But nonetheless

we know that there were several automatons that is things that could move automatically and replicate at least human emotion Now Leibnitz he is also the father of calculus along with

Newton had an idea of a calculus of human ideas We will come to this entry go on later

But Leibnitz idea was that every thought that we have is a combination of a few axiomatic

simple basic units of thoughts We do not go that way but you can see an analogy with how modern machines are working How do

modern versions work Modern machines or modern computers work on the basis that everything

can be broken down zeros and ones So the basic question that machine learning people have

been asking in a very broader sense is to see whether all that we do in terms of thinking

I terms of creativity can it be broken down into a few elemental products or a few elemental

processes So Leibnitz speculation was that this is indeed possible okay Charles Babbage made the first

or at least conceptualised the first analytical engine modern-day computers are very very

similar to whatever Babbage actually conceptualised This was right back in 1837 the fructification

of this was in 1940s when the first computers were made My point is whatever we think of

computation today it is actually amazing if even great work from the 1940s they could

have conceptualised what computers are doing today In some ways you can even think of computers today as artificial intelligence In that you

can book a ticket you can record speech you can play music you can see movies this is

a wide variety of tasks mind you All of those are done on one single humble computer In

some sense this is already artificial intelligence what we are going to do at least in this course

is a little bit further We know that while this is going on it is not really thinking it is not really learning Our idea is to see if we can make algorithms which can actually

learn So here is the birth of artificial intelligence 1914 was the first chess playing machine all

it was doing was king rook and king ending those of you know how to play chess can we

will know that if one party has a king and rook and another party just has a king you can checkmate And always So here was a machine which would actually do that okay Surprisingly

enough the first driverless car was right back in 1925 this was made with the help of

the US Army and I think Francis Houdina this is not Houdini of the magician fame this was

a different person altogether So he made the first driverless car however this was radio controlled See even this was

quite amazing to people right back then The first theoretical progress happened in the

1940s we had the first artificial neurons we will see this when we will come to neural

networks Turing also first proposed the idea of theory of computation and the idea of a

universal computer The idea of a universal computer is the idea

that one single computer can do all computable tasks This seems obvious to us we have sort of grown used to it but it was not obvious right in

the beginning that every single computation can actually be done You can think of a ticket

booking as a competition you can think of playing a video as also a computation all the of that can be done on a single universal computer was not obvious at all So Alan Turing

was the person who actually pioneered this idea Shannon also came up with information

theory this is now also being used extensively within ideas in machine learning and of course

in a lot of places like signal processing etc 1950s was in some sense a place when artificial intelligence took off whether you read science-fiction

from that period or whether you read just normal research people writing So Norbert

Wiener came with this idea of cybernetics it was very very popular Lewinski is a very

famous researcher in the field he made the first neural net machine stochastic neural

automatic reinforcement calculator a SNARC And Simon and Newell Simon was a Nobel Prize

winner he worked for all his life for decision theory and affectively artificial intelligence

what we call artificial intelligence today So they made an automatic theory proving theorem proving machines So the first machine was

this was not suffer but it was actually hardware the first neural network machine was made

by Lewinski in 1950s 1956 was the first coining of the term artificial intelligence a famous

conference call the Dartmouth conference Simon Newell and Shannon all 3 of them participated

is this And the sentiment was really really positive Rosenblatt was the first person who

came up with a 2 layer artificial neural network called the Perceptron he unfortunately died

really really young I think he died in the 1960s at the age of 40 or something

Now another researcher Arthur Samuel just to define machine learning a little bit more

precisely than we did in the beginning is to say that machine learning is a field that gives the computers the ability to learn This is what is key the ability to learn we will

define learning itself a little bit later without being explicitly programmed So we

will come to this distinct and shortly okay So here is the idea of being explicitly programmed This is the idea of an expert system so let

us say you want to find out or you want to make an algorithm that detects grammar errors

okay So some way to say it is you start putting all the rules that you know of English grammar

let us say into the machine saying that if this follows that you know if it is a singular

person then you put S if it is multiple people put R But what happens shortly is in many

cases it is a really really hard to program all the rules And we will see some examples as we go on forth

You will see that it is quite hard to do it even in grammar It is not called clear how it is that human beings are able to recognise different grammars for different languages

Most of us in Indians speak at least 2 to 3 languages and most of us can seamlessly

switch from the grammar of one language to the grammar of another language and it is not clear what sort of roles before low So expert systems work well when the rules are

clear and when the rules are not clear is typically when we would like to use machine learning okay

So when in 1960s when people thought of artificial intelligence mostly they were thinking of

rule-based systems Even in chess for simple games like tic-tac-toe you can use usually

quickly give the rule that will let you win or at least not lose But in more complex such as chess or Go it is actually hard and that is what people have been finding out So but

early days of artificial intelligence whether it was playing chess making organic chemistry

models solving world problems in algebra etc etc and even in understanding natural language

some progress was made but it was not good enough precisely because of this

Because of the fact that these were completely rule-based you had a rule for every single case And if you did not give a rule the company does not know what to do There was also theoretical

progress we will see this algorithm is what makes machine learning algorithms or at least

neural networks learn Back propagation this was available way back in 1969 that is nearly

50 years ago today in 2018 But the progress was good and people were very optimistic here

is Herbert Simon again he is a Nobel Prize winner as I said He thought that the machines should be capital by 1980s of doing any work a man can do

Unfortunately till today machines cannot even do what let us say mosquitoes or rats can

do So we are nowhere close Nonetheless we have made a lot of progress which is why the course is here We also had Lewinski saying something extremely positive it was very very

similar But whenever you have such kind of a hype cycle you should always know that you

are going to get into problems So what was known as the first AI winter happened between 1974 and 1980

The problems were that all the results that were there even within chess were primarily

for simple toy problems The kind of exercise problems we do in any course simple problems

The computational power was also exceedingly low today Cellphones probably have greater

power than most of the big mainframe machines had back then So the computational problems we are looking at in those days were really really low Combinatorial explosion especially

for rule-based systems okay So like I said there is no simple finite set of rules within

which you can explain every single case of grammar okay It is really really difficult Even for chess you cannot give a rule for every single situation

it is just too expensive Combinatorial explosion means when you go from a small problem to

a larger problem the number of choices which are expanding is really large If I only have

one knight rook or knight ending I have only 3 pieces that is slightly less complex If I have 5 pieces you have things start growing in terms of factorial is in terms of power

laws and that is very very hard for a computer to handle Okay So the key to even modern-day machine learning is this idea that commonsense is nearly impossible

to program A baby today can look at a face and recognise that it is father or it is its

mother But you actually program in and to say why it is that it is its father or why

it is that is the same person regardless of this person changing their clothes changing the expression changing the way they speak then growing older having a beard or not having

a beard It is really impossible for you to program every single case in But somehow magically

human beings tend to do this really really rapidly okay So how is it that this happens is of course it is a long-standing problem in cognition

it is still an open problem Nonetheless we do know that it is nearly impossible to program

in this explicitly That is at least when we have rule-based programs you cannot do this

that easily In fact probably I will be bold enough to say that you cannot do it at all

which is where machine learning steps in One other thing that happened during 1974 to 1980

was Lewinski's Perceptron showed up He made a very simple argument which we will make later to this was not an unknown argument

The fact that very simple neural networks which are called single layer neural networks and not solve some knowledge problems It is kind of obvious as you will see later on in

this course it is a very obvious argument He also made an argument that multilayer neural

networks are hard to train so that was also made

What we mean by training is that somehow it is difficult to kind of Automatically program which is what we are going to go into this course

That this is very difficult to do if you have more than one layer If you do not understand that a player that is okay we will see this later on during the course Anyway this actually

set up a panic but it will be set up a panic in conjunction with the fact that already results were hyped And what happened was there was a lot of loss of government funding in

AI and obviously most of the research work stopped Nonetheless some brave pioneers continued and as usual you know you have this kind of

boom bust boom cycle that keeps on going on in many fields machine learning is just one

example One boom was between 80 and 87 we actually had the first driverless car then

this was if I remember right this is by Mercedes And there was huge funding if I understand

it currently about 750 million pounds were invested in driverless cars right back then

And it did not come to anything as we do now now of course Tesla is taking over Google Lexus etc

There was a book because primarily some builders are using expert systems popularisation of

back propagation and as usual Lewinski said that you know we are going to probably going

to go into first and it did happen Between 87 and 93 the PC became very popular you are

not looking at large computation but individual people are able to do small word processing and etc for their needs They are not looking at grand aims like General Machine becoming

smart etc Again there were a total funding cuts usually precedes long winter in AI Now between 94

and 2000 people got a little bit smarter and there was a long period of consolidation Some

of you might recall or you might know that in 1997 IBM's Deep Blue beat Kasparov in chess

this was still a rule-based system it was not a machine learning system A machine learning

system playing chess has come only last year again by Google's people it is an extension

of Alpha Go I think it is called Alpha Zero So in 1997 Deep Blue beat Kasparov in chess almost all stock fish etc all chess engines

that exist today are still rule-based There was also simultaneously development in theory

including probability theory information theory optimisation theory good optimisation algorithms

which will be using in this course And of course there was the stupendous power of Moore's

Law Moore's law is the law that number of transistors doubles every 2 years Now an adapted version of that is you know we have sort of made it a tool On y-axis here

is the number of calculations that you can do per second per unit money that you are

spending That this is a log log scale on the y-axis you have to log scale this is a linear

scale And you can see that you have exponential growth of computational power At least computational

power in terms of the cost it takes for you to do this So integrated circuits are there

we are now predicting quantum computation which is at least supposed to help or not necessarily interpretation but at least in some types of algorithm it is supposed to

help even 40 planning Google and other companies have invested very deeply into it We also have GPUs that allows

much cheaper computation So Moore's Law in terms of growth of computational exponential

growth of computational power has really helped So 2000 to 2012 I would call the quiet years

but they were quiet in terms of artificial content but there were very significant developments

The company Google was born not only Google there were several search engines of course Google used it really well and came up with good algorithms for that

What it helped was there is a large number of searches and when there is a large amount

of searches there is a large amount of data So the key thing for machine learning machine

learning is very very data hungry as you will see was the amount of data that subjected

itself through statistical analysis and statistical techniques This is what happened between 2000

and 2012 there was an Internet boom once again a lot of people offering a lot of products a lot of people offering a lot of data images videos all these came together and you had

a large database on which you could train By train you will see what we mean later on in the course Also we had Nvidia with GPUs

these are very packed computational power horses And we had specifically good results

if you brave researchers were continuing their work using deep networks Another thing that

people did very pragmatically was instead of looking at some bold aims like Machine becoming intelligent on its own they started looking at very specific outcomes

Instead of even saying stuff like I want a machine that will recognise everything when it looks at it which would be the computer vision problem It would say can I have a machine

that can read a postcard and read the pin code Just in that case it needs recognise

only 10 digits 0 through 9 So can such specific outcomes really helped in getting good results

That is what led to the boom even today you will see specific algorithms for vision specific

algorithms for natural language processing in a specific area Instant of saying something that can read your mail and understand it can I have a spam

filter That is a specific outcome and in such cases you can actually find out which kind

of machine learning model works better than the others So this led to good very good results

and led to a positive growth of the field 2005 once again we had autonomous driving

for about 135 miles of course without any interruption What people are trying for now is more sophisticated Can you actually drive on a street while people are moving around

and we have had good results with Tesla

Also one important result I think this was in 2011 or 2012 I am not sure IBM's Watson

beat the Jeopardy champion this is a quiz show and it is a nontrivial quiz show it is not a simple language show It has puns it has plays on words so the machine needs to

understand more than how human sense in some sense okay And after this period of Uhh consolidation

we are now within what would be called AI spinning We do not know whether it is ended

some people are already saying it is not it but anyway all of us know that we are known growth cycle right now There has been a lot of what has sort of pushed it there is a lot and a lot of private funding

not just government funding in fact governments are catching up Google has its deep mind IBM

has its own thing has its Watson Facebook has its things Microsoft has its things and all of them have been fairly democratic about sharing their resources also There has been

rapid growth in computational power as I said earlier GPUs etc And a very important portion

of it has been rapid growth in data Widow Facebook has data Google has data Microsoft has our data and they have been doing a lot

of data mining legally Hopefully and this has led to a lot of growth in machine learning

itself A lot of people have done voluntary distributed network in let us say tagging

images some of us have done it even though semi-voluntarily choosing captchas Captchas

are these things you know you will have digits like N123 etc that is just popped up in order

to identify whether your human being or a Robot But what it has done is it has also

helped machines being trained Each time you say this a machine recognises that this kind of image probably means N this

kind of image probably means 1 So that has been used also in training So voluntarily

and semi-voluntarily we have been doing a lot of training for these machines and that

has led to a lot of data Games also have led to a lot of data

The inflection point you know the point where a lot of people identified as the real growth

of machine learning in AI at least the modern boom cycle is sometimes in 2012 There is a

challenge called the image net challenge which we will see them become to CNNs So this is

just a vision recognition challenge out of a thousand categories of images you have to say which one is which is this a cat is it a dog is it a building etc So most of the

algorithms at that point all of the algorithms at that point which had the winning work in some sense traditional vision based rule-based algorithm

So 2012 was the first time that a machine learning algorithm called convolutional neural

networks which one this is called the algorithm which one is called Alex net which we will

be covering in detail later on in the course This was 2012 and since then every single

year the algorithm which has won is only a machine learning algorithm Alex net showed

a huge jump in performance based from about 12 percent jump from previous algorithms And

this is when people sat up and took notice and since then the field is just taken off

very rapidly The number of people who have got in within the last 5 or 6 years is just huge People

who started out doing their PhD's in 2012 without a machine learning algorithm have

done things that are surprisingly chaste and the course of their PhD This is not very long but a lot of the material that will be covering in the courts will actually be from the last

3 or 4 or 5 years We will be covering classical techniques but will be also covering what

has been specifically done in the last few years Which is another reason that we are asking that you also understand how to read research

papers because the field is still developing it is still in some sense early days and you

need to know how to keep up with the literature So part of the language and the techniques that we will be introducing you through the course is for us to make sure that you can

actually read the papers understand and maybe implement them yourself in some application of your interest

So here are 2 results that have been the reasons for people getting kind of worries This is

a machine versus humans this is the same thing Jeopardy this is a quiz show in United States

I do not believe this is there in India as yet But it is not quite sure and it is an

involved language based quiz show of course it is knowledge-based also But there are puns etc and you have to understand allusions very clearly So the thing that wants was IBM's

Watson and it is a machine learning-based algorithms or at least semi-rule-based semi-machine

learning based Another thing that was frankly a shock for many people in the field was Alpha Go Go is

a game which has simpler rules than just but it is known to be combinatorially much more

harder to solve It is a 19 by 19 board where people simply place a white or a black piece

But nonetheless it was often known to be a hard problem problem in AI that is chess was

thought to be essentially solved by a rule-based system People thought that there would be

no machine which will beat a Go champion in maybe another 10 years Even Google when they came up with Alpha were not sure that they would actually win their

aim while playing Lee Sedol the Go champion at that point was simply to maybe win a game

or 2 And then to learn and make the system better but it actually beat Lee Sedol handsdown

And after that I think they have retired Alpha Go records it is just has been so good in betting every single human being Practically it is unbeatable at this point So one thing

that is also true about machine learning algorithms is sometimes it is hard to know how good they will be

So these are the 2 of the recent results and they have been cause for again a recent hype

cycle So the question is is there anything different this time and the answer is yes

at least there are few tangible things that are different this time We generally have better technology okay and computational problem that is available today is exponentially such

as compared to the difference between let us say 1950s and 1970s between 2 cycles So

we have GPUs then we have all sorts of futuristic computational technology that people are proposing

Whether they come through or not Moore's Law is still holding you know it is breath and so it is kind of running its course already It is going for but we have different architecture

We have really really big data we are practically drowning in data and probably we need better

algorithms to handle this kind of data that we have An important portion of the current

boom cycle is we have had democratisation of resources A lot of people have a lot of

algorithms can be run on a simple laptop that is accessible to most people today Between 75000 rupees to 1 lakh Laptop can actually have a simple GPU a simple card Uhh

that can actually do a very good job and even some of the simpler algorithms can work Also

many of the commercial companies have been very very generous with their software All

the packages whether this Google's Tensor flow Facebook's pie torch etc etc IBM Microsoft

all of them have made a lot of resources available to the common public So the open source movement

has also taken off and this has led to a lot of software which is being available to the

common public We will be using a few of these through this course which actually makes a person come

up to speed very quickly Even if they do not know how to code something from scratch they can use existing packages are at least use a view of those algorithms We will be seeing

that later on in this course And there have been generally better algorithms even though

there are variations of prior algorithms we do genuinely have better algorithms today

So our focus for the rest of the course is the algorithms portion What we are going to

really look at is what organisms work and under what circumstances do they work

We are going to look at algorithms as if they are models okay So if you have done any engineering

problem at all you will know that sophisticated processes we have various models If you are

in fluid mechanics you will have various models of how a fluid behaves you will have various models of how turbulence we have Similarly if you are an solid mechanics you would have

seen various models for how stress strain should be model etc etc In every field an

ideal gas law is a model Ohm's Law is a model for how current and voltage and resistance

play together So all these are models with think of algorithms as if they are models Modelling what the specific

input and output relationship in any problem that you are looking at So suppose I have a mail and I am going to classify this as spam or not spam So there is something that

is going on in my range which is modelling this What sort of model will work best is

what we are going to look at through this course Under what circumstances if you have a vision problem what kind of range of models do we have

If you have a time sequence problem what kind of models we have this is what we are going

to look at for the rest of this course Thank you

Machine Learning for Engineering and Science Applications