MACHINE LEARNING - INTRODUCTION

DEFINITION:

Machine Learning is a set of methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data or to perform other kinds of decision making under uncertainty (such as planning how to collect more data).

LEARNING PROBLEM:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

Example: a computer program that learns to play checkers P in this case is measured by its ability to win

T in this case is playing checkers games

E in this case is obtained by playing games against itself

In general, to have a well-defined learning problem we must identify these three features:

1.         The class of tasks (T)

2.         The measure of performance to be improved (P)

3.         The source of experience (E)

Checkers learning problem:

·       Task T – playing checkers

·       Performance measure P – percent of games won against opponents

·       Training experience E – playing practice games against itself

 

Handwriting recognition learning problem:

·       Task T – recognizing and classifying handwritten words within images

·       Performance measure P – percentage of words correctly classified

·       Training Experience E – a database of handwritten words with given classifications

Robot driving learning problem:

·       Task T – driving on public four lane highways using vision sensors

·       Performance measure P – average distance travelled before an error (as judged by human overseer)

·       Training experience E – a sequence of images and steering commands recorded while observing a human driver

E-mail classification:

·       Task T: Categorize email messages as spam or legitimate.

·       Performance P: Percentage of email messages correctly classified.

·       Training Experience E: Database of emails, some with human-given labels

Machine Learning – Introduction

 

TABLE OF CONTENTS

1.         Machine Learning – Introduction

2.         Machine Learning Framework

3.         AI/ML Problem – phases

4.         ML Workflow, Tools and Landscape

5.         Dataset and subsets

6.         Machine Learning Types

7.         Supervised Learning

8.         Supervised Learning Types

9.         Classification Vs Regression

10.     Performance metrics

11.     Classification Algorithms

a.      Logistic Regression

b.      K-Nearest Neighbor (KNN)

c.      Decision Tree

d.      Random Forest – Gradient Boosting

e.      Naïve Bayes

f.       Support Vector Machines (SVM)

g.      Gradient Descent

h.      Neural Networks

12.     Neural Networks

a.      Perceptron

b.      Multi-Level Perceptron (MLP)

c.      Artificial Neural Networks (ANN)

d.      Deep Neural Networks (DNN)

13.     Activation Functions

14.     Dropout

15.     Backpropagation

16.     Cross Validation

17.     Model Compression

18.     Loss Function

19.     Deep Learning/Deep Neural Networks

a.      Convolutional Neural networks (CNN)

b.      Recurrent Neural Networks (RNN)

c.      Restricted Boltzmann Machine (RBM)

d.      Deep Belief Networks (DBN)

e.      Autoencoders

20.     Convolutional Neural Networks

a.      Core idea

b.      Convolution layer

c.      Various terms associated with CNN

d.      Pooling layer and types of pooling

e.      CNN Architecture

f.       Optimization of CNN

g.      Working of CNN

h.      Applications

21.     Recurrent Neural Networks

a.      RNN and limitations

b.      Differences with CNN

c.      Long Short-Term Memory (LSTM)

d.      Gated Feedback Recurrent Neural Networks (GRU)

22.     Restricted Boltzmann Machine (RBM)

23.     Deep Belief Networks (DBN)

24.     Autoencoders

a.      Types

b.      Applications

c.      Convolutional Autoencoders

d.      Denoising Autoencoders

e.      Deep Autoencoders

f.       Variational Autoencoders (VAE)

g.      Sparse Autoencoders

25.     Siamese Neural Networks

26.     Generative Adversarial Networks (GAN)

27.     Regression Algorithms

a.      Linear Regression

b.      Multiple Linear Regression

c.      Polynomial Regression

d.      Ridge Regression

e.      Lasso Regression

f.       ElasticNet Regression

28.     Unsupervised Learning

29.     Unsupervised Learning Algorithms

a.      Clustering

b.      K-Means Clustering

c.      Hierarchical Clustering

d.      DBSCAN

e.      Gaussian Mixtures

f.       Spectral Clustering

30.     Dimensionality Reduction

a.      Feature Engineering

b.      Multicollinearity

c.      Factor Analysis

d.      Principal Component Analysis (PCA)

e.      Linear Discriminant Analysis (LDA)

f.       Isometric Mapping (IsoMap)

g.      Locally Linear Embedding (LLE)

h.      t-distribution Stochastic Neighborhood Embedding (t-SNE)

31.     Semi-supervised Learning

32.     Semi-supervised learning algorithms:

a.      Self Training

b.      Generative Models

c.      S3VMs

d.      Graph Based Algorithms

e.      Multiview algorithms

33.     Reinforcement Learning

a.      Introduction

b.      Approaches

c.      Markov Decision Process

d.      Q-learning

e.      Application of RL

34.     Other topics

a.      Word2Vec

b.      Generalization

c.      Overfitting

d.      Regularization

e.      Bias

f.       Variance

g.      Occam’s Razor

h.      Bag of Words (BoW)

i.       Recommender Systems

j.       Ensemble Methods

k.      Natural Language Processing (NLP)