Recap: Machine Learning

What we saw in the previous chapter?

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Repeat the last slide of the previous chapter

A jargon cloud

How do you make sense of all the jargon?

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Make an actual cloud of all keywords that we will see through the course (list down all the keywords from the table of contents on my course homepage

From jargons to jars

What are the six jars of Machine Lerarning

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Show six empty or shaded jars

* I want images which look like this but this is an expensive image and not available for free

Data data everywhere

What is the fuel of Machine Learning?

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Data data everywhere

How do you feed data to machines ?

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We encode all data into numbers - typically high dimension

For instance, in this course you will learn to embed image and text data as large vectors

Data entries are related - eg. given a MRI scan whether there is a tumour or not

Include a table that shows two/three MRI scans in first col, shows large vectors in second column, 1/0 for last column of whether there is tumour or not

Include a table that shows two/three reviews in first col, shows large vectors in second column, 1/0 for last column for whether review is positive or negative

Title the columns as x and y

tumor/no tumor

Now show a matrix of numbers here

the last column is tumor/no tumor

an arrow here

Show the ML system from your Slide 10 of Expert Systems

an arrow here

Data curation

Where do I get the data from?

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 +  मुंबई

 =     मुंबई

Data data everywhere

What is the fuel of Machine Learning?

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Show data jars

Tasks

What do you do with this data?

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We want to show that we can do different tasks with same data by making different input output papers. Show the following input output pairs one-by one. Feel free to redefine the input output pairs given below suitably

1. From product description to structured specs

2. From specs + reviews to writing FAQs

3. From specs + reviews + FAQs to question answering

4. From specs + reviews + personal data to recommendations

Amazon product data with description, reviews, product specs

Input

Output

Tasks

What do you do with this data?

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We want to show that we can do different tasks with same data by making different input output papers. Show the following input output pairs one-by one. Feel free to redefine the input output pairs given below suitably

1. From photos identify people, places, activities

2. From posts + personal data recommend posts

3. From video detect profanity, etc.

Facebook profiles and photos

Input

Output

Tasks

What do you do with this data?

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Different types of tasks: 

1. Supervised

- Classification - text or no text

- Regression - fitting bounding boxes (more later) 

2. Unsupervised

- Clustering - clustering news articles by similarity

- Generation - deep art, deep poetry

Most of the realworld ML tasks (90%) are supervised. This course will exclusively focus on this class of problems. Except for easter eggs. 

In supervised ML it is about finding y given x

Supervised

1. Show data matrix with x and y

2. images with and without signboards

Here show a SVM like line separator with signboard images on one side and no-signboard on other

Classification

Tasks

What do you do with this data?

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Different types of tasks: 

1. Supervised

- Classification - text or no text

- Regression - fitting bounding boxes (more later) 

2. Unsupervised

- Clustering - clustering news articles by similarity

- Generation - deep art, deep poetry

Most of the realworld ML tasks (90%) are supervised. This course will exclusively focus on this class of problems. Except for easter eggs. 

In supervised ML it is about finding y given x

Supervised

1. Show data matrix with x and left_x, left_y, width, height

2. images with signboards and bounding boxes

Image

Regression

Output 

lx, lr, w, h values

Now show bounding box in the images

Tasks

What do you do with this data?

(c) One Fourth Labs

Different types of tasks: 

1. Supervised

- Classification - text or no text

- Regression - fitting bounding boxes (more later) 

2. Unsupervised

- Clustering - clustering news articles by similarity

- Generation - deep art, deep poetry

Most of the realworld ML tasks (90%) are supervised. This course will exclusively focus on this class of problems. Except for easter eggs. 

In supervised ML it is about finding y given x

Unsupervised

1. Show data matrix with only x

2. images with and without signboards

Here show 3 to 4 clusters such that yellowfins signboard  in one cluster, blue in another and so on

Clustering

Tasks

What do you do with this data?

(c) One Fourth Labs

Different types of tasks: 

1. Supervised

- Classification - text or no text

- Regression - fitting bounding boxes (more later) 

2. Unsupervised

- Clustering - clustering news articles by similarity

- Generation - deep art, deep poetry

Most of the realworld ML tasks (90%) are supervised. This course will exclusively focus on this class of problems. Except for easter eggs. 

In supervised ML it is about finding y given x

Unsupervised

1. Show data matrix with only x

Show picasso style images

Show output of deep art or of the painting which recently got sold for x million dollars

Generation

Tasks

What do you do with this data?

(c) One Fourth Labs

Different types of tasks: 

1. Supervised

- Classification - text or no text

- Regression - fitting bounding boxes (more later) 

2. Unsupervised

- Clustering - clustering news articles by similarity

- Generation - deep art, deep poetry

Most of the realworld ML tasks (90%) are supervised. This course will exclusively focus on this class of problems. Except for easter eggs. 

In supervised ML it is about finding y given x

Unsupervised

Show many trump tweets

Show output of Deep Trump

Generation

Tasks

What do you do with this data?

(c) One Fourth Labs

Photo of Andrew Ng

Supervised Learning has created 99% of economic value in AI

In this course

Classification

Regression

RHS from classification slide

RHS from regression slide

Tasks

What do you do with this data?

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Show data, tasks jars

What is the mathematical formulation of a task?

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\( x \)

\( y \)

bat

car

dog

cat

Models

ship

Models

What are the choices for \( \hat{f} \) ?

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- Show some points sampled from this function

- Say that there is some complex relation between x

- Naively I assumed that its y=mx + c

- no matter how I adjust m and c I can't make f and \( \hat{f} \) equal (apparently you can make a video of this in python itself)

- Let's try another function which is a polynomial

- another function...better...better better

\( y  = mx + c \) 

\( y  = ax^2 + bx + c \) 

\( y  = \sigma(wx + b) \) 

\( y  = Deep\_NN(x) \) 

\( \hat{y}  = \hat{f}(x) \) [our approximation]

\( \left [\begin{array}{lcr} 0.2\\ 0.1\\ 0.7\\ ....\\0.8 \end{array} \right]\)

\( \left [\begin{array}{lcr} 2.2\\ 3.1\\ 0.7\\ ....\\4.8 \end{array} \right]\)

\( x \) 

\( y \) 

\( y  = ax^3 + bx^2 + cx + d \) 

\( y  = ax^4 + bx^3 + cx + d \) 

I want to say that the true data was drawn from this function

These should be the points drawn from the function shown

Use Y_hat everywhere

In this course

\( y  = Deep\_CNN(x) \) ...​

\( y  = RNN(x) \)​ ...

Models

Why not just use a complex model always ?

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\( \left [\begin{array}{lcr} 0.2\\ 0.1\\ 0.7\\ ....\\0.8 \end{array} \right]\)

\( \left [\begin{array}{lcr} 0.4\\ 0.2\\ 1.4\\ ....\\1.6 \end{array} \right]\)

\( x \) 

\( y \) 

This will be replaced by a simple line 

We will show animation how it will be easy to fit a line but difficult to fit 100 degree polynomial

\( y  = mx + c \) [true function, simple]

\(y = ax^{100} + bx^{99} + ... + c \) [our approximation, very complex]

Later in this course

Bias-Variance Tradeoff

Overfitting

Regularization

Models

What are the choices for \( \hat{f} \) ?

(c) One Fourth Labs

Add model jar

Loss Function

How do we know which model is better ?

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\( \left [\begin{array}{lcr} 0.2\\ 0.1\\ 0.7\\ ....\\0.8 \end{array} \right]\)

\( \left [\begin{array}{lcr} 0.4\\ 0.2\\ 1.4\\ ....\\1.6 \end{array} \right]\)

\( x \) 

\( y \) 

?

Show plots for true f and f_1 f_2 f_3... From the plots it will not be clear but from the columns it will be clear. You can take the points sampled from the sin_x function as in my bias variance lecture. f1, f2, f3 can be 3 different 25 degree polynomial as in that

The functions should be such that on visual inspection it should not be clear which is the closest to the true function

Now, on the next slide we will show the loss function

Then it is clear! Why is it clear? because you are computing some number and numbers are easy to interpret

\( \left [\begin{array}{lcr} 0.4\\ 0.2\\ 1.4\\ ....\\1.6 \end{array} \right]\)

\( \left [\begin{array}{lcr} 0.4\\ 0.2\\ 1.4\\ ....\\1.6 \end{array} \right]\)

\( \left [\begin{array}{lcr} 0.4\\ 0.2\\ 1.4\\ ....\\1.6 \end{array} \right]\)

\( \hat{f_1}(x)  = a_1x^{25} + b_1x^{24} + ... + c_1x + d_1 \) 

\( \hat{f_2}(x)  = a_1x^{25} + b_1x^{24} + ... + c_1x + d_1 \) 

\( \hat{f_3}(x)  = a_1x^{25} + b_1x^{24} + ... + c_1x + d_1 \) 

Substitute different values for a1 b1, c1, d1 in the 3 equations

\( \begin{array}{lcr} 1\\ 2\\ 3\\ ....\\n \end{array} \)

the values for y should come from the sine function

put values for y1, y2, y3 as computed by the 3 functions defined below

\( \mathscr{L}_1 = \sum_{i=1}^{n} (y_i - \hat{f}_1(x_i))^2 \) 

\( \mathscr{L}_2 = \sum_{i=1}^{n} (y_i - \hat{f}_2(x_i))^2 \) 

\( \mathscr{L}_3 = \sum_{i=1}^{n} (y_i - \hat{f}_3(x_i))^2 \) 

In this course

Square Error Loss

Cross Entropy Loss

KL divergence

Text

cartoon images of

3 friends

Loss Function

How do we know which model is better ?

(c) One Fourth Labs

\( \left [\begin{array}{lcr} 0.2\\ 0.1\\ 0.7\\ ....\\0.8 \end{array} \right]\)

\( \left [\begin{array}{lcr} 0.4\\ 0.2\\ 1.4\\ ....\\1.6 \end{array} \right]\)

\( x \) 

\( y \) 

\( \left [\begin{array}{lcr} 0.4\\ 0.2\\ 1.4\\ ....\\1.6 \end{array} \right]\)

\( \left [\begin{array}{lcr} 0.4\\ 0.2\\ 1.4\\ ....\\1.6 \end{array} \right]\)

\( \left [\begin{array}{lcr} 0.4\\ 0.2\\ 1.4\\ ....\\1.6 \end{array} \right]\)

\( \begin{array}{lcr} 1\\ 2\\ 3\\ ....\\n \end{array} \)

the values for y should come from the sine function

put values for y1, y2, y3 as computed by the 3 functions defined below

\( \mathscr{L}_1 = \sum_{i=1}^{n} (y_i - \hat{f}_1(x_i))^2 \) 

\( \mathscr{L}_2 = \sum_{i=1}^{n} (y_i - \hat{f}_2(x_i))^2 \) 

\( \mathscr{L}_3 = \sum_{i=1}^{n} (y_i - \hat{f}_3(x_i))^2 \) 

In this course

Square Error Loss

Cross Entropy Loss

KL divergence

cartoon images of

3 friends

Show loss function computation only for \( \hat{f}_1 \) here as an example... Its ok to just show 3-4 terms in the summation (same terms which are there in the array on the LHS)

= some value after the computation shown on RHS

= some value after the computation shown on RHS

= some value after the computation shown on RHS

Loss Function

What does a loss function look like ?

(c) One Fourth Labs

Add jar for loss function and have a recap

Who will give us the parameters ?

Learning Algorithm

How do we identify parameters of the model?

(c) One Fourth Labs

Animation:

- first the data matrix appears

- then the model equation with a,b,c as parameters appears

- then the friend appears

- then the loss function appears

- now the red cross appears and then the friend disappears

- now the box for the learning algorithm appears

- now the logo for search appears

- now an animation where the values of a,b,c are adjusted till the loss reaches some low value

- data, model and loss function feed int othe pink box

Show a a matrix with 3 inputs: budget* (0 to 1), box office collection* (0 to 1), action scene time+ (0 to 1)

* the unit is 100 crores so 0.1 means 1 crore (mention this in the head row of the table

+ the unit here is 100 minutes

The output is the imdb rating

Show x and y above the header

\( \hat{f_1}(x)  = 3.5x_1^2 + 2.5x_2^{3} + 1.2x_3^{2} \) 

cartoon images of 3 friends

\( \hat{f_1}(x)  = ax_1^2 + bx_2^{3} + cx_3^{2} \) 

\( \mathscr{L}_1 = \sum_{i=1}^{n} (y_i - \hat{f}_1(x_i))^2 \) 

Show a gear box inside this to indicate learning algorithm

Show adjustable scales for a,b,c and create a python video here if you adjust the scale the loss function value changes and you hit some value for which the error is zero. You can actually cheat by creating the y value using some  values of a,b,c so that you can then get 0 error for these values of error

logo for search

Learning Algorithm

How do you formulate this mathematically ?

(c) One Fourth Labs

Animation:

- data, model, pink box, loss function, logo for search and animation for a,b,c appears as it if from previous slide

- now the cross and the message appears

- now the text in red appears (but make it black)

Show a a matrix with 3 inputs: budget* (0 to 1), box office collection* (0 to 1), action scene time+ (0 to 1)

* the unit is 100 crores so 0.1 means 1 crore (mention this in the head row of the table

+ the unit here is 100 minutes

The output is the imdb rating

Show x and y above the header

\( \hat{f_1}(x)  = ax_1^2 + bx_2^{3} + cx_3^{2} \) 

\( \mathscr{L}_1 = \sum_{i=1}^{n} (y_i - \hat{f}_1(x_i))^2 \) 

Show a gear box inside this to indicate learning algorithm

Show adjustable scales for a,b,c and create a python video here if you adjust the scale the loss function value changes and you hit some value for which the error is zero. You can actually cheat by creating the y value using some  values of a,b,c so that you can then get 0 error for these values of error

logo for search

In practice, brute force search is infeasible

Find \(a, b, c \) such that

is minimized

Learning Algorithm

How do you formulate this mathematically ?

(c) One Fourth Labs

Animation:

Only the green part gets added on this slide (but show it in black except for the tick mark)

Show a a matrix with 3 inputs: budget* (0 to 1), box office collection* (0 to 1), action scene time+ (0 to 1)

* the unit is 100 crores so 0.1 means 1 crore (mention this in the head row of the table

+ the unit here is 100 minutes

The output is the imdb rating

Show x and y above the header

\( \hat{f_1}(x)  = ax_1^2 + bx_2^{3} + cx_3^{2} \) 

\( \mathscr{L}_1 = \sum_{i=1}^{n} (y_i - \hat{f}_1(x_i))^2 \) 

Show a gear box inside this to indicate learning algorithm

Show adjustable scales for a,b,c and create a python video here if you adjust the scale the loss function value changes and you hit some value for which the error is zero. You can actually cheat by creating the y value using some  values of a,b,c so that you can then get 0 error for these values of error

logo for search

Many optimization solvers are available

\(min_{a,b,c}\)

Learning Algorithm

How do you formulate this mathematically ?

(c) One Fourth Labs

Animation:

Only the green part gets added on this slide (but show it in black except for the tick mark)

Show a a matrix with 3 inputs: budget* (0 to 1), box office collection* (0 to 1), action scene time+ (0 to 1)

* the unit is 100 crores so 0.1 means 1 crore (mention this in the head row of the table

+ the unit here is 100 minutes

The output is the imdb rating

Show x and y above the header

\( \hat{f_1}(x)  = ax_1^2 + bx_2^{3} + cx_3^{2} \) 

\( \mathscr{L}_1 = \sum_{i=1}^{n} (y_i - \hat{f}_1(x_i))^2 \) 

Many optimization solvers are available

\(min_{a,b,c}\)

In this course

Gradient Descent ++

Adagrad

RMSProp

Adam

Learning Algorithm

How do we identify parameters of the model?

(c) One Fourth Labs

Add jar for Learning Algorithm

Evaluation

How do we compute a score for our ML model?

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Top - 1

Evaluation

What are some other evaluation metrics ?

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Top-3

Evaluation

How is this different from loss function ?

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Evaluation

Should we learn and test on the same data?

(c) One Fourth Labs

Show some training data from before, preferably something containing image classification

\( \hat{f_1}(x)  = ax_1^2 + bx_2^{3} + cx_3^{2} \) 

\( \mathscr{L}_1 = \sum_{i=1}^{n} (y_i - \hat{f}_1(x_i))^2 \) 

Show a gear box inside this to indicate learning algorithm

\(min_{a,b,c}\)

Now show test data

Now show formula for accuracy here

Animation:

Only the green part will com on animation, the rest of it will be shown at the beginning itself

Evaluation

How is this different from loss function ?

(c) One Fourth Labs

Add jar for evaluation

​

Putting it all together

How does all the jargon fit into these jars?

(c) One Fourth Labs

Show six jars and a foundation (3 blocks for Lin. Alg., Prob., Calculus)  on which these jars are placed

Here's the hard part now: Remember the word cloud at the beginning. Now you need to organize them into these 6 jars and foundation:-) It's ok if you miss out a few things. Juts put them on the side. I will see where to fit them. Show the word cloud in the background as faded

Data, democratisation, devices

Why ML is very successful?

(c) One Fourth Labs

Show the same diagram as previous slide

1) On top of Evaluation show "standardised", and show logos of ImageNet, Pascal VOC, WMT

2) On top of Learning Algorithms and loss functions show "improvised"

3) On top of models show "democratised"

4) Now on top of 1st jar write "Abundance"

Typical ML effort

How to distribute your work through the six jars?

(c) One Fourth Labs

Show the same diagram as previous slide

1) Now put this box to cover the last 4 jars

2) Now on top of 1st 2 jars say "Your job"