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Domain Track: Data Science and Machine Learning

Domain Track: Data Science and Machine Learning

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Dr. Sujata Chakravarty

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Domain Track Title :Data Science and Machine Learning

Total Credits ( T-P-P): 26(2-9-15)

Courses Division:

 

  1. Data Analysis and Visualisation Using Python -CUML2000- 4(0+1+3)

  2. Machine Learning using Python -CUML2001- 4(1+2+1)

  3. ML for Predictive Analysis -CUML2002-4(1+2+1)

  4. ML for Image Analytics -CUML2003- 6(0+4+2)

  5. ML for Hyperspectral Imaging -CUML2004- 6(0+4+2)

  6. Internship -CUML2005- 4(0+0+4)

  7. Project -CUML2006-4(0+0+4)

Note: Only one course has to be opted from course-4 & course-5.

Domain Track Objectives:

  • Understand the scope, stages, applications, effects and challenges of ML.

  • Understand the mathematical relationships within and across ML algorithms and the paradigms of supervised and unsupervised learning.

Domain Track Learning Outcomes:

  • Ability to Create and incorporate ML solutions in their respective fields of study.

  • Ability to design and implement various machine learning algorithms in a range of real-world applications.

  • Ability to design product/ publish article/ file patent

Domain Syllabus:

Course 1: Data Analysis and Visualisation Using Python (0+1+3)

 

1.1 Story Board Development:-

  • The objective and flow of the story to be understood through cases.

1.2 Data Reading using Python Functions;-

  • Python libraries: Pandas, NumPy, Plotly, Matplotlib, Seaborn, Dash.

  • Data collection from online data sources

  • Web scrap, data formats such as HTML, CSV, MS Excel.

  • Data compilation, arranging and reading data, data munging

1.3 Data Visualisation using Python Libraries:-

  • Using graphs- Scatterplot, Line chart, Histogram, Bar chart, Bubble chart, Heatmaps .

  • Dashboard Basics- Layout, Reporting, Infographics, Interactive components, live updating.

Projects

  • COVID 19

  • World Development Indicators

  • ERP dashboarding

  • Details of Social/ Empowerment schemes of Govt.

References:

Course 2: Machine Learning using Python (1+2+1)

 

2.1 Application and Environmental-setup:- 

  • Applications of Machine Learning In different fields (Medical science, Agriculture, Automobile, mining and many more).

  • Supervised vs Unsupervised Learning based on problem Definition.

  • Understanding the problem and its possible solutions using IRIS datasets.

  • Python libraries suitable for Machine Learning(numpy, scipy, scikit-learn, opencv)

  • Environmental setup and Installation of important libraries.

2.2 Regression:- 

  • Linear Regression

  • Non-linear Regression

  • Model Evaluation in Regression

  • Evaluation Metrics in Regression Models

  • Multiple Linear Regression

  • Feature Reduction using PCA

  • Implementation of regression model on IRIS datasets.

2.3 Classification:- 

  • Defining Classification Problem with IRIS datasets.

  • Mathematical formulation of K-Nearest Neighbour Algorithm for binary classification.

  • Implementation of K-Nearest Neighbour Algorithm using sci-kit learn.

  • Classification using Decision tree.

  • Construction of  decision trees based on entropy.

  • Implementation of Decision Trees for Iris datasets .

  • Classification using Support Vector Machines.

  • SVM for Binary classification

  • Regulating different functional parameters of SVM using sci-kit learn.

  • SVM for multi class classification.

  • Implementation of SVM using Iris datasets .

  • Implementation of Model Evaluation Metrics using sci-kit learn and IRIS datasets.

2.4 Unsupervised Learning:- 

  • Defining clustering and its application in ML .

  • Mathematical formulation of K-Means Clustering.

  • Defining K value and its importance in K-Means Clustering.

  • Finding appropriate K value using elbow technique for a particular problem.

  • Implementation of K-Means clustering for IRIS datasets

Projects

  • To be defined based on respective study area of student.

References:

 

Text Book:

  1. Ethem Alpaydin, Introduction to Machine Learning, Second Edition, http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12012.

Web Resource:

  1.  https://towardsdatascience.com/beginners-guide-to-machine-learning-with-python-b9ff35bc9c51

Course 3: ML for Predictive Analysis (1+2+1)

 

Project/Task: (Choose one among four Tasks)

  • Time Series Analysis

  1. Prediction of Financial Time Series data

  2. Covid-19 cases prediction based time series analysis.            

  • Health Care System

  1. Cancer detection

  2. Skin disease detection

Concept Required:

 

3.1 Data pre-processing:-

  • Accessing / collecting the datasets from different online repository.

  • Missing values handling, noise reduction using, finding Correlation between features, outlier elimination.

3.2 Feature extraction and selection: -

  • Principal component analysis (PCA)

  • Linear discriminant analysis (LDA)

3.3 Model building: -

  • Regression (Linear, Polynomial, multiple, logistic), Decision Tree, Random Forest.

  • Artificial Neural Network (Feed Forward Neural Network, Back Propagation Neural Network).

3.4 Performance measures: -

  • Perdition: Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE).

  • Classification: Confusion Matrix (TN, TP, FP, FN), Sensitivity, Specificity, Overall Accuracy, (Receiver Operating Characteristic) ROC Curve.

Course 4: ML for Image Analytics (0-4-2)

 

Project/Task: (Choose one among six Tasks)

  • Detection of optometry diseases using retinal fundus imaging.

  1. Diabetic Retinopathy

  2. Glaucoma

  3. Cataract

  • Detection of various diseases using X-ray imaging.

  1. Covid19

  • Leaf disease classification using RGB images.

  1. Tomato leaf

  2. Potato leaf

Concept Required:

 

4.1 Image Pre-processing:-

  • Accessing individual pixels using matrix concept

  • Image resize, grey scale conversion, Colour channel splitting

  • Histogram equalisation (CLACH).

4.2 Image Feature Extraction: -

  • Edge detection (Sobel, Canny), Morphological operations

  • Image segmentation, Image Thresholding, Binary conversion

  • Cluster based segmentation

  • Feature extraction based on size, shape and colour

  • Feature extraction using predefined functions: SIFT, SURF, STAR, ORB.

  • Feature Extraction using convolutional neural network (CNN).

4.3 Creation of Feature Matrix by combining Extracted Features: -

  • Matrix flattening, Horizontal stacking, Vertical stacking, padding.

  • Splitting the feature matrix (training/testing)  and labelling.

4.4 Classification algorithms: -

  • Support vector machine (SVM)

  • Different kernels of SVM (linear, polynomial, radial basis function).

  • Gradient Boosting (GB)

  • Multi-layer Perceptron (MLP), deep learning.

Course 5: Ml for Hyperspectral Imaging (0-4-2)

 

Project/Task: (Choose one among four Tasks)

  • Agriculture

  1. Crop yield prediction.

  2. Crop quality prediction

  3. Soil health monitoring

  • Mining

  1. Iron ore quality prediction

Concept Required:

 

5.1 Introduction to Remote Sensing: -

  • Multi-Spectral Imagery (MSI)

  • Hyperspectral Imagery (HSI)

5.2 Scientific Principles:

  • Physics of imaging spectroscopy

  • Electromagnetic propagation

  • Sensor physics

  • Atmospheric Corrections.

5.2 Hyperspectral Concepts and System Trade-offs:-

  • Signal-to-Noise ratio (SNR)

  • Spectral resolution, sampling.

5.3 HSI Data Processing Techniques:-

  • Spectral angle mapping

  • Principal Component Analysis (PCA)

  • Minimum Noise Fraction (MNF)

  • Spectral feature fitting.

5.4 Classification Techniques:-

  • Support Vector Machine (SVM)

  • Partial Least Squares Regression (PLSR)

  • Neural Network

  • Deep learning and CNN

5.5 Clustering Techniques:-

  • K-mean clustering

Course 6: Project (0-0-4)

Course 7: Internship (0-0-4)

Session Plan for the Entire Domain:

Data Analysis and Visualisation Using Python (0+1+3) 60 hrs

Session 1

Course objective, outcome, methodology and assessment. 

Why data visualisation

https://www.youtube.com/watch?v=YaGqOPxHFkc

https://www.youtube.com/watch?v=3JWK5gRI9p0

Session 3

Practice 

Environmental setup - Anaconad and Jupyter notebook, Anaconda Navigator and Libraries Installation

https://www.youtube.com/watch?v=beh7GE4FdnM

Session 5 & 6

Project - 1

For Project -1, the student group has to define the objective/s of the study, identify the data that will be needed and the source of such data

Make Presentations groupwise

Session 10 & 11

Project - 1

Data collection and sorting for the assigned project

Pandas Tutorial 1. What is Pandas python? Introduction and Installation- https://www.youtube.com/watch?v=CmorAWRsCAw

Pandas Tutorial2. Dataframe and Series Basics- Selecting row and column- https://www.youtube.com/watch?v=zmdjNSmRXF4

Pandas Tutorial 3: Different Ways Of Creating DataFrame - https://www.youtube.com/watch?v=3k0HbcUGErE

Python Pandas Tutorial 4: Read Write Excel CSV File- https://www.youtube.com/watch?v=-0NwrcZOKhQ

Importing data in python - Read excel file - https://www.youtube.com/watch?v=lco-r5CgvhY

Pandas Tutorial 8 | How to import HTML data in Python | Importing HTML data in Python - https://www.youtube.com/watch?v=ndwuUzgAiPY

Pandas Tutorial 13, Crosstabs - https://www.youtube.com/watch?v=I_kUj-MfYys

Session 12 & 13

Practice

Basics of Numpy

https://www.youtube.com/watch?v=xECXZ3tyONo

Complete Python NumPy Tutorial (Creating Arrays, Indexing, Math, Statistics, Reshaping) - https://www.youtube.com/watch?v=GB9ByFAIAH4

Session 16 & 17

Practice

Basic of Matplotlib

https://www.youtube.com/watch?v=MbKrSmoMads&pbjreload=10

Matplotlib Tutorial 1 - Introduction and Installation- https://www.youtube.com/watch?v=qqwf4Vuj8oM

Matplotlib Tutorial 2 - format strings in plot function - https://www.youtube.com/watch?v=zl5qPnqps8M

Matplotlib Tutorial 3 - Axes labels, Legend, Grid- https://www.youtube.com/watch?v=oETDriX9n1w

Matplotlib Tutorial 4 - Bar Chart - https://www.youtube.com/watch?v=iedmZlFxjfA

Matplotlib Tutorial 5 - Histograms - https://www.youtube.com/watch?v=r75BPh1uk38

Matplotlib Tutorial 6 - Pie Chart - https://www.youtube.com/watch?v=GOuUGWGUT14

Matplotlib Tutorial 7 - Save Chart To a File Using savefig - https://www.youtube.com/watch?v=XLJHkCn48lM

Plotting real-time data using Python - https://www.youtube.com/watch?v=GIywmJbGH-8

Session 18, 19, 20 & 21

Project (work on Project -1)

Work on the projects assigned using Python Libraries

Pandas Tutorial 1. What is Pandas python? Introduction and Installation- https://www.youtube.com/watch?v=CmorAWRsCAw

Pandas Tutorial 3: Different Ways Of Creating DataFrame - https://www.youtube.com/watch?v=3k0HbcUGErE

Python Pandas Tutorial 4: Read Write Excel CSV File

https://www.youtube.com/watch?v=-0NwrcZOKhQ

Importing data in python - Read excel file - https://www.youtube.com/watch?v=lco-r5CgvhY

Pandas Tutorial 8 | How to import HTML data in Python | Importing HTML data in Python - https://www.youtube.com/watch?v=ndwuUzgAiPY

Pandas Tutorial 13, Crosstabs - https://www.youtube.com/watch?v=I_kUj-MfYys

 

Session 22 & 24

Practice 

Plotly

https://plotly.com/python/basic-charts/

Graphing Library - https://plotly.com/python/

Plotly Python - Plotly multi line chart| Plotly Python data visualization- https://www.youtube.com/watch?v=pfhBbJ2MnMI

Plotly Data Visualization in Python | Part 13 | how to create bar and line combo chart - https://www.youtube.com/watch?v=AQG4RQolUC8

Plotly Web based visualisation - https://www.youtube.com/watch?v=B911tZFuaOM

Session 25, 26, 27 & 28

Project (Project-1)

Work on Project

Interim Presentation

Session 29 & 30

Session 31 & 32

Practice

Web Scrapping

https://www.youtube.com/watch?v=mKxFfjNyj3c

Web scraping in Python (Part 4)_ Exporting a CSV with pandas - https://www.youtube.com/watch?v=Zh2fkZ-uzBU

Web scraping in Python (Part 2)_ Parsing HTML with Beautiful Soup - https://www.youtube.com/watch?v=zXif_9RVadI

Webscraping - Mode, Median, Mean, Range, and Standard Deviation- https://www.youtube.com/watch?v=mk8tOD0t8M0

Web Scraping Dynamic Graphs to CSV Files using Python - https://www.youtube.com/watch?v=NYK_1bVoBfU

Session 33 & 34

Practice

Solve the 10 problem (started from session 7)

-students will submit the assignment (in groups)

(upload the assignment ...done by Prof. Ramana)

Session 35 & 36

Practice 

Dashboard Basics

https://www.youtube.com/watch?v=e4ti2fCpXMI

Create Presentation Slides from Jupyter - https://www.youtube.com/watch?v=utNl9f3gqYQ

Dash and Python 1_ Setup -https://www.youtube.com/watch?v=Ldp3RmUxtOQ

Dash and Python 2_ Dash Core Components - https://www.youtube.com/watch?v=NM8Ue4znLP8

Dash and Python 3_ Using CSS - https://www.youtube.com/watch?v=x9mUZZ19dl0

Session 37 & 38

Practice

Dash (plotly) and Python

https://www.youtube.com/watch?v=Ldp3RmUxtOQ

Dash in 5 Minutes  - https://www.youtube.com/watch?v=e4ti2fCpXMI

How to Create a Slideshow using Jupyter+Markdown+Reveal.js- https://www.youtube.com/watch?v=EOpcxy0RA1A

ipython dashboard - https://www.youtube.com/watch?v=LOWBEYDkn90

Plotting real-time data using Python - https://www.youtube.com/watch?v=GIywmJbGH-8

Session 38 & 39

Session 40 & 41

 

Session 42 & 43

Project

Work on Project - 1 to make dash boards

Session 44, 45 & 46

 

Project

Final presentation of Project -1

Session 47 & 48

Project -2

Start Project - 2 (ERP Dash Board)

Define the objective and prepare the flow chart

Session 49 & 50

Project

Make presentations on the objective and flow chart of Project-2

Session 50 & 51

Project

Work on Project - 2

Session 52 & 53

Project

Make interim presentation on Project - 2

Session 54 & 55

Project

Work on Project - 2

Session 56 & 58

Project

Final Presentation on Project -2

Session 59 & 60

Project

Make final changes on Project -1 & Project -2 to make it ready for External Evaluation

Machine Learning using Python (1+2+1) 56 hrs

Session-1

 

Applications of Machine Learning

https://www.youtube.com/watch?v=ahRcGObyEZo

Session-2,3

 

Supervised vs Unsupervised Learning based on problem Definition

https://www.youtube.com/watch?v=cfj6yaYE86U

Session-4,5

 

Understanding the problem and its possible solutions using IRIS datasets.

https://www.youtube.com/watch?v=FLuqwQgSBDw

Session-6,7

 

Mathmatical library in Python numpy and its  functions

https://www.youtube.com/watch?v=EkYrfV7M1ks

Session-8,9

 

Science library in Python scipy and its  functions

https://www.youtube.com/watch?v=k8s-R3csOt0

session-10,11

 

ML library in Python scikit-learn and its  functions.

https://www.youtube.com/watch?v=bwZ3Qiuj3i8

Session-12

 

Defining student specific Project

Session-13

 

Linear Regression

https://www.youtube.com/watch?v=E5RjzSK0fvY

Session-14

 

Non-linear Regression

https://www.youtube.com/watch?v=sKrDYxQ9vTU

Session-15

 

Model Evaluation

https://www.youtube.com/watch?v=c68JLu1Nfkw

Session-16

 

Evaluation Metrics in Regression Models

https://www.youtube.com/watch?v=iLfgZfRGisE

Session-17,18

 

Multiple Linear Regression

https://www.youtube.com/watch?v=dQNpSa-bq4M

Session-19

 

Feature Reduction using PCA

https://www.youtube.com/watch?v=FgakZw6K1QQ

Session-20

 

Implementation of regression model on IRIS datasets.

https://www.youtube.com/watch?v=hd1W4CyPX58

Session-21

 

Defining Classification Problem with IRIS datasets.

https://www.youtube.com/watch?v=Y17Y_8RK6pc

Session-22,23

 

Create the train/test set using scikit-learn using scikit-learn

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html

Session-24,25

 

Confussion Matrix, Accuraccy, Sensitivity, specificity

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html

Session-26

 

Mathematical formulation of K-Nearest Neighbour Algorithm for binary classification.

https://www.youtube.com/watch?v=4HKqjENq9OU

Session-27,28

 

Implementation of K-Nearest Neighbour Algorithm using sci-kit learn.

https://www.youtube.com/watch?v=6kZ-OPLNcgE

Session-29,30

 

Classification using Decision tree.

https://www.youtube.com/watch?v=7VeUPuFGJHk

Session-31,32

 

Construction of  decision trees based on entropy.

https://www.youtube.com/watch?v=7VeUPuFGJHk

Session-33,34

 

Implementation of  Decision Tree using sci-kit learn

https://www.youtube.com/watch?v=PHxYNGo8NcI

Session-35,36

 

Classification using Support Vector Machines.

https://www.youtube.com/watch?v=Y6RRHw9uN9o

Session-37,38

 

SVM for Binary classification

https://www.youtube.com/watch?v=T5zJHhTO1FA

Session-39,40

 

Regulating different functional parameters of SVM using sci-kit learn.

https://www.youtube.com/watch?v=93AjE1YY5II

Session-41,42

 

SVM for multi class classification.

https://www.youtube.com/watch?v=kH6T_XL10-A

Session-43,44

 

 Implementation of Support Vector Machines.

https://www.youtube.com/watch?v=zEabrO9l1vg

Session-45,46

 

Defining clustering and its application in ML

https://www.youtube.com/watch?v=V-8E0KhNrI8

Session-47,48

 

Mathematical formulation of K-Means Clustering.

https://www.youtube.com/watch?v=YWgcKSa_2ag

Session-49,50

 

Defining K value and its importance in K-Means Clustering.

https://www.youtube.com/watch?v=4b5d3muPQmA

Session-51,52

 

Implementation of K-Means Clustering in  Scikit-learn

https://www.youtube.com/watch?v=asW8tp1qiFQ

Session-53,54

 

Finding appropriate K value using elbow technique for a particular problem.

https://www.youtube.com/watch?v=IEBsrUQ4eMc

ML for Predictive Analysis (1+2+1) 56 hrs

Session-1,2

 

Accessing / collecting the datasets

https://www.youtube.com/watch?v=Gp_qv317Gew

Session-3,4

 

Missing values handling

https://www.youtube.com/watch?v=EaGbS7eWSs0

Session-5,6

 

Noise reduction in datasets using python

https://www.youtube.com/watch?v=rzR_cKnkD18

Session-7,8

 

Finding Correlation between features

https://www.youtube.com/watch?v=BCuIq206lrA

Session-9,10

 

Outlier elimination

https://www.youtube.com/watch?v=rzR_cKnkD18

Session-11,12

 

Principal component analysis (PCA)

https://www.youtube.com/watch?v=kApPBm1YsqU

Session-13,14

 

Linear discriminant analysis (LDA)

https://www.youtube.com/watch?v=azXCzI57Yfc

Session-15,16,17

 

Project Review-1 (Datasets and analysis of datasets)

Session-18,19,20

 

Regression (Linear, Polynomial)

https://www.youtube.com/watch?v=ro5ftxuD6is

Session-21,22,23

 

Linear Regression Multiple Variables

https://www.youtube.com/watch?v=J_LnPL3Qg70

Session-24,25,26

 

Logistic Regression (Multiclass Classification)

https://www.youtube.com/watch?v=J5bXOOmkopc

Session-27,28

 

Decision Tree

https://www.youtube.com/watch?v=PHxYNGo8NcI

Session-29,30

 

Random Forest

https://www.youtube.com/watch?v=ok2s1vV9XW0

Session-31,32

 

Artificial Neural Network

https://www.youtube.com/watch?v=aircAruvnKk

Session-33,34,35

 

Feed Forward Neural Network

https://www.youtube.com/watch?v=aircAruvnKk

Session-36,37,38

 

Back Propagation Neural Network

https://www.youtube.com/watch?v=Ilg3gGewQ5U

Session-39,40,41

 

Project review-2(Model selection and implementation)

Session-42,43

 

Root Mean Square Error (RMSE)

https://www.youtube.com/watch?v=zMFdb__sUpw

Session-46,47

 

Confusion Matrix (TN, TP, FP, FN)

https://www.youtube.com/watch?v=-dsoGHvk7II

Session-48,49

 

Sensitivity, Specificity, Overall Accuracy

https://www.youtube.com/watch?v=UsOv0DcXk6w

Session-50,51,52

 

Receiver Operating Characteristic ROC Curve

https://www.youtube.com/watch?v=4jRBRDbJemM

Session-53,54,55,56

 

Project review-3(Validation of the model)

ML for Image Analytics (0-4-2) 86hrs

Session-1,2,3

 

Accessing individual pixels using matrix in image

https://www.youtube.com/watch?v=j-ZLDEnhT3Q

Session-4,5,6

 

Image resize and splitting of image

https://www.youtube.com/watch?v=cWHW9MnX_F4

Session-7,8,9,10

 

Grey scale conversion and mathematical implementation

https://www.youtube.com/watch?v=sTxGKae0Fck

Session-11,12,13

 

Colour channel splitting

https://www.youtube.com/watch?v=PHIyhdcLsL8

Session-14,15,16,17

 

Histogram equalisation (CLACH)

https://www.youtube.com/watch?v=jWShMEhMZI4

Session-18,19,20,21

 

Edge detection (Sobel, Canny)

https://www.youtube.com/watch?v=h8Yp3M8SX2M

Session-22,23,24

 

Morphological operations

https://www.youtube.com/watch?v=xSzsD4kXhRw

Session-25,26,27,28

 

Image segmentation

https://www.youtube.com/watch?v=kIVk0IhDMwY

Session-29,30,31

 

Image Thresholding

https://www.youtube.com/watch?v=Zf1F4cz8GHU

Session-32,33,34

 

Binary conversion of segmented image

https://www.youtube.com/watch?v=X4icNTQSCss

Session-35,36,37,38

 

Cluster based segmentation

https://www.youtube.com/watch?v=6CqRnx6Ic48

Session-39,40,41,42

 

Feature extraction based on size, shape and colour

https://www.youtube.com/watch?v=IG3UkAqHnQI

Session-43,44,45,46

 

Feature extraction using predefined functions: SIFT, SURF, STAR, ORB

https://www.youtube.com/watch?v=USl5BHFq2H4

Session-47,48,49,50

 

Feature Extraction using convolutional neural network (CNN).

https://www.youtube.com/watch?v=Q21fWIdHGFM

https://www.youtube.com/watch?v=WvoLTXIjBYU

Session-51,52,53,54

 

Matrix flattening in Python

https://www.youtube.com/watch?v=6XLXmzv6ajQ

Session-55,56,57

 

Horizontal stacking and Vertical stacking

https://www.youtube.com/watch?v=ksqPbrS-b78

Session-58,59,60,61

 

Creation of Feature matrix by padding required number of zeros and ones.

https://www.youtube.com/watch?v=94ZmuGhuBIk

Session-62,63,64

 

Splitting the feature matrix (training/testing)

https://www.youtube.com/watch?v=fwY9Qv96DJY

Session-65,66,67

 

Labelling the datasets

https://www.youtube.com/watch?v=V2e0cygY9Vg

Session-68,69,70

 

Support vector machine (SVM) in image analysis.

https://www.youtube.com/watch?v=Y6RRHw9uN9o

Session-71,72,73,74

 

Different kernels of SVM (linear, polynomial, radial basis function)

https://www.youtube.com/results?search_query=Different+kernels+of+SVM+%28linear%2C+polynomial%2C+radial+basis+function%29

Session-75,76,77

 

Gradient Boosting (GB)

https://www.youtube.com/watch?v=jxuNLH5dXCs

Session-78, 79, 80

 

Multi-layer Perceptron (MLP)

https://www.youtube.com/watch?v=AZEfmoWBXwg

Session-881,82,83

 

Deep learning for Image classification using CNN

https://www.youtube.com/watch?v=AACPaoDsd50

Session-84,85,86

 

Deep learning for Image classification using  RNN

https://www.youtube.com/watch?v=iMIWee_PXl8

ML for Hyperspectral Imaging (0-4-2) 86 hrs

Session-1,2,3

 

Multi-Spectral Imagery (MSI)

https://www.youtube.com/watch?v=b0webdvlySo

Session-4,5,6

 

Hyperspectral Imagery (HSI)

https://www.youtube.com/watch?v=e6hbmSfPnMQ

Session-7,8,9

 

Hyperspectral remote sensing and its applications

https://www.youtube.com/watch?v=2LNXeUS25Vw

Session-10,11,12,13

 

Physics of imaging spectroscopy

https://www.youtube.com/watch?v=PMk85e90p6s

Session-14,15,16

 

Electromagnetic propagation

https://www.youtube.com/watch?v=lTjSdnEcJV8

Session-17,18,19

 

Sensor physics

https://www.youtube.com/watch?v=3iaFzafWJQE

Session-20,21,22,23

 

Atmospheric Corrections

https://www.youtube.com/watch?v=4VVxuQI5yqQ

Session-24,25,26

 

Signal-to-Noise ratio (SNR)

https://www.youtube.com/watch?v=ZOchEgeuobM

Session-27,28,29

 

Spectral resolution

https://www.youtube.com/watch?v=Hu1T_rEb7D0

Session-30,31,32,33

 

Image sampling and quantization in digital image processing

https://www.youtube.com/watch?v=0_255tTnhLQ

Session-34,35,36,37

 

Spectral angle mapping using different methods

https://www.youtube.com/watch?v=xq8bOmWQXqU

Session-38,39,40,41

 

Principal Component Analysis (PCA) in HSI Imaging

https://www.youtube.com/watch?v=VaiolMYETmE

Session-42,43,44,45

 

Minimum Noise Fraction (MNF) using different methods

https://www.youtube.com/watch?v=6sEx9cUvLtw

Session-46,47,48,49

 

Spectral feature fitting

https://www.youtube.com/watch?v=5hX7kU52cEU

Session-55,51,52,53

 

Support Vector Machine (SVM) for HSI

https://www.youtube.com/watch?v=F5C6FexFfjE

Session-54,55,56,57

 

Partial Least Squares Regression (PLSR)

https://www.youtube.com/watch?v=WKEGhyFx0Dg

Session-58,59,60,62

 

Spectral Clustering  using Python

https://www.youtube.com/watch?v=Z10BXWPFnas

Session-62,63,64,65

 

 Hyperspectral image classification using multiple spectral and spatial features

https://www.youtube.com/watch?v=vxh53StWaUk

Session-67,68,69,70

 

Models and Algorithms for Hyperspectral Image Processing

https://www.youtube.com/watch?v=RZu1LHumbiQ

Session-71,72,73,74

 

Applied Hyperspectral Imaging Fundamentals and Case Studies

https://www.youtube.com/watch?v=Z_Zub7wJTFs

Session-75,76,77,78

 

Classification For Hyperspectral Remote Sensing Imaging Using Neural Network

https://www.youtube.com/watch?v=Co2Dw-HRFw8

Session-79,80,81,82

 

Hyperspectral image classification using Deep learning and CNN

https://www.youtube.com/watch?v=7pdOBBrRqIQ

Session-83,84,85,86

 

Case study-Implements dimensionality reduction on hyper spectral image(Indian Pines) with classification.

Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python

Gate Process for Project

Gate 0:       Problem Identification

 

Gate 1:       Data Collection

 

Gate 2:       Model Development

 

Gate 3:       Testing and Validation

 

Gate 4:       Publication, Patent, Product

Our Main Teachers

Dr. Sujata Chakravarty

HoD & Associate Professor, Department of CSE, SoET
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Dr. Sujata Chakravarty is a Senior Member of IEEE. Her research area includes multidisciplinary fields like Application of Computational Intelligence and Evolutionary Computing Techniques in the field of Financial Engineering, Bio-medical data classification, Smart Agriculture, Intrusion Detection System in Computer-Network, Analysis and prediction of different financial time series data. She is a reviewer of many […]

MANOJ KUMAR BEHERA

Asst. Prof. Dept of CSE
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Manoj Kumar Behera, M. tech. in Computer Science, NIT Rourkela, Qualified GATE in 2008. His research area includes application of machine learning and image processing in the fields of smart agriculture and Bio-medical applications. He has published about 20 articles in many international journals and conferences.