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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

MLCU2000 - Data Science and Machine Learning

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

CUML2000 - Data Analysis and Visualisation Using Python-  4 (0+1+3)
CUML2001 - Machine Learning using Python - 4 (1+2+1)
CUML2010 - ML for Predictive Analysis - 4 (1+2+1)
CUML2011 - ML for Image Analytics - 4 (0+2+2)
CUML 2009 - Mathematics for ML - 3 (2+1+0)
CUML 2008 - IoT Analytics - 4 (0+2+2)
CUML 2012 - Digital video Processing - 3 (0+2+1)
CUML2004 ML for Hyperspectral imaging 6 (0+4+2)
CUML2005 Internship 4 (0+0+4)
CUML2006 Project 4 (0+0+4)

Course Outline

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       Code                                        Course Title                                         T-P-Pj (Credit)            Prerequisite

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MLCU2000                     Data Science and Machine Learning                      2-9-15                                 NIL

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Objective

● 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.
● Able to get jobs in AI/ML field

Course Outcome

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     COs                                   Course outcomes                                                                   Mapping COs with POs
                                                                                                                                               (High-3, Medium-2, Low-1)

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CO1 Able to gain knowledge on design and implementation of various machine learning algorithms in a range of real world applications. PO1 (3)
CO2 Able to analyze prediction and classification using different ML and deep learning methods PO2(3)
CO3 Ability to solve real world problems using ML solutions in their respective fields of study. PO2(3)
CO4 Ability to design product PO3 (2), PO5(2)
CO5 Able to do research for publishing articles/
file patents		 PO4 (3)

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Course content

Data Analysis and Visualization 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:

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

ML for Predictive Analysis (1+2+1)

https://careerfoundry.com/en/blog/data-analytics/regression-vs-classification/

Project/Task: (Choose two projects, Prediction and Classification)

  • Time Series Analysis
  • Health Care System

Concept Required:

 

3.1 Data pre-processing: -

  • Accessing / collecting the datasets from different online repository.

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

  • Label Encoding / Encoding the categorical data

  • Splitting the dataset

  • Data Normalization

3.2 Learning Algorithms: -

  • Supervised Learning Algorithms

  • Unsupervised Learning Algorithms

3.3 Feature extraction and selection: -

  • Principal Component Analysis (PCA)

  • Linear Discriminant Analysis (LDA)

  • Different Feature Selection Techniques / Algorithms

3.4 Model building: -

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

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

  • Convolutional Neural Network

  • Other Pre-Trained Models

3.5 Performance measures: -

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

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

3.6 Reading and Writing Research Articles ML

https://www.predictiveanalyticsworld.com/book/pdf/Predictive_Analytics_by_Eric_Siegel_Excerpts.pdf

ML for Image Analytics (0-2-2)

 

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.

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

Digital video Processing (0+2+1)

6.1 Fundamentals of Video Processing: Digital Video Acquisition, Principles of Color Video,

Video Camera, Video Display, Analog Vs Digital Video: Progressive Vs Interlaced scans, Signal, Bandwidth Characterization of a Digital Video Signal.

Practice:

  • Read and play video files
  • Extract frames from video files
  • Combine frames to create a video file

 

6.2 Fourier Analysis of Digital Video Signals: Spatial and Temporal resolution, Fourier Analysis of Digital Video Signals, Spatial-Temporal Sampling: Temporal Frequency Response and Flicker Perception. Spatial Frequency Response, Spatiotemporal Frequency Response, Smooth Pursuit Eye Movement

Practice:

  • Applying fourier transformation on video
  • Time domain analysis
  • Frequency domain Analysis

6.3 Digital Video Formats: Significance of Video Formatting, Data rate and bandwidth trade-off, File Formats: MP4, MOV, WMV, AVCHD, FLV, AVI, WebM, MKV

Digital Video Compression Standards: Digital Video Compression Metrics, Digital Video Storage Precisions, Significance of Video compression, Video Compression Codec’s: Motion JPEG, JPEG 2000, H.264/MPEG-4 AVC, VP8, HEVC, H.265 High Efficiency Video Codec.

Practice:

  • Conversion of video files from one format to another.
  • Using Motion JPEG Codec
  • Using MPEG-4 Codec
  • Using H.265 Codec

 

6.4 Digital Video Editing Basics: Video Editing Types- Online, Offline, Linear, Non-linear, Assemble, Insert, Rough-cut, Video Shot Transition Effects: Cut, Fade, Wipe, Dissolve, B-roll, Video Shot Boundary Detection Methods: pixel differences, statistical differences, histogram comparisons, edge differences  and motion vectors. Video Shot Detection Performance Metrics: ROC Curves, Recall, Precision, F-Measure

Practice:

  • Video Shot Detection using pixel Difference
  • Video Shot Detection using Histogram based methods
  • Video Shot Detection using  Edge based methods
  • Video Shot Detection using  Motion Vectors

Project List

  • Creating a VIDEO object detection system
  • Vehicle detection in Videos using OpenCV and Python
  • Detecting faces in live camera feed with identification of the person.

 

TEXT BOOK:

  1. Rafael C Gonzalez and Richard E Woods, “Digital Image Processing’’, Pearson Education, 3rd Edition, 2009.
  2. Handbook of Image and Video processing - Al Bovik (Alan C Bovik), Academic Press,

REFERENCE BOOK:

  1. Fundamentals of Digital Image Processing”, Anil K. Jain, PHI, 1995.
  2. “Digital Image Processing”, William. K.Pratt, Wiley Interscience, 2nd Ed, 1991.

 

Mathematics for ML (2+1+0)

 

When Models Meet Data:-

  • Data, Models, and Learning

  • Empirical Risk Minimization

  • Parameter Estimation

  • Probabilistic Modelling and Inference

  • Directed Graphical Models

  • Model Selection

Linear Regression:-

  • Problem Formulation

  • Parameter Estimation

  • Bayesian Linear Regression

  • Maximum Likelihood as Orthogonal Projection

Dimensionality Reduction with Principal Component Analysis:-

  • Problem Setting

  • Maximum Variance Perspective

  • Projection Perspective

  • Eigenvector Computation and Low-Rank Approximations

  • PCA in High Dimensions

  • Key Steps of PCA in Practice

  • Latent Variable Perspective

 Density Estimation with Gaussian Mixture Models:-

  • Gaussian Mixture Model

  • Parameter Learning via Maximum Likelihood

  • EM Algorithm

  • Latent-Variable Perspective

Classification with Support Vector Machines:-

  • Separating Hyperplanes

  • Primal Support Vector Machine

  • Dual Support Vector Machine

  • Kernels

  • Numerical Solution

Practice:

  • Curve Fitting in Python.

  • Exploratory Data Analysis in Python.  Kernel Density Estimation in Python.

  • Probability Distribution Function Plotting in Python.

  • Cumulative Distribution Function Plotting in Python.

  • Dimensionality Reduction and Feature Extraction in Python.

References:

  1. Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.

  2. https://youtube.com/playlist?list=PLLy_2iUCG87D1CXFxE-SxCFZUiJzQ3IvE

Project (0-0-4)

IoT Analytics (0+2+2)

 

8.1  Defining IoT Analytics and Challenges

        IoT

        Benefits of Deploying IoT

        End to End IoT architecture

        IoT challenges

8.2  IoT Protocols

        8.2.1  Wireless Protocol

                     Connectivity Protocols (when Power is Limited)

                     Bluetooth Low Energy (BLE)

                     Zigbee

                     LoRaWAN

                     NFC

       8.2.2  Connectivity Protocols (when Power is Not a problem)

                    Wifi

      8.2.3   Data Communication Protocol

                    MQTT

                    Web-Socket

                    HTTP

8.2  Sensors

        Types of Sensors based on communication-I2C, SPI

        Types of Sensors based on Application

8.3  Overview of 32 -bit Controller

         ESP8266

         ESP32

         Raspberry Pi

8.4  AWS IoT for Cloud

         AWS IoT Core services

         AWS IoT Analytics services

         AWS DynamoDB Services

8.5  Thingspeak for IoT

         Getting and posting Data to IoT Cloud using ESP devices

         Posting Data to IoT Cloud using Raspberry Pi

8.6  ThingWorx for Industrial IoT

         Building Dashboard on Thingworx platform

         Binding the senor value to the dashboard

Text Book:

  1. Minteer, Andrew. Analytics for the Internet of Things (IoT). Packt Publishing Ltd, 2017.

Reference Books:

      2. Geng, Hwaiyu, ed. Internet of things and data analytics handbook. John Wiley & Sons, 2017.

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 2

Story telling using Visuals & Infographics

https://venngage.com/blog/9-types-of-infographic-template/#1

https://www.edugrad.com/tutorials/learn-data-visualization-using-python/15

https://towardsdatascience.com/storytelling-with-data-a-data-visualization-guide-for-business-professionals-97d50512b407

Tips on good visuals

https://statedashboard.odisha.gov.in/

https://data.gov.in/

https://www.youtube.com/watch?v=4pymfPHQ6SA

Project Groups:

Students will be divided into groups and assigned projects. Each group will do two projects.

Session 3

Practice 

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

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

Session 4 & 5

Practice

Python Fundamentals, Use Case - Data Analysis, Exploring and learning assignments on Jupyter Notebook

https://towardsdatascience.com/data-visualization-say-it-with-charts-in-python-138c77973a56

https://towardsdatascience.com/plotting-with-python-c2561b8c0f1f

https://towardsdatascience.com/introduction-to-data-visualization-in-python-89a54c97fbed

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 7, 8 & 9

Practice 

Data collection/importing and reading using Python function of different types of files, i.e. CSV, HTML, Excel - Get CSV data files from source and read them, get HTML file and read, Get Excel sheet and read

https://perso.telecom-paristech.fr/eagan/class/igr204/datasets

https://www.youtube.com/watch?v=eWFwe41LyWk

https://towardsdatascience.com/wrangling-data-with-pandas-27ef828aff01

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

https://www.youtube.com/watch?v=Ycq3sDg6ji0

Sorting data, Missing values & Munging data

https://www.youtube.com/watch?v=-dwjEfv2R50

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

https://www.youtube.com/watch?v=T11QYVfZoD0

https://www.askpython.com/python/python-csv-module

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 14 & 15

Practice 

Basic of Pandas

https://www.youtube.com/watch?v=dcqPhpY7tWk

https://www.tutorialspoint.com/python_pandas/python_pandas_pdf_version.htm

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

Practice

Seaborn - Scatter Chart, Bubble Chart, Gapminder

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

https://www.tutorialspoint.com/seaborn/seaborn_tutorial.pdf

Interactive Data Visualization - https://www.youtube.com/watch?v=VdWfB30QTYI

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

Practice 

Interactive charts/Maps using Bokeh,

Dash board using Dash

https://www.youtube.com/watch?v=o4TB6LTPDaY

https://towardsdatascience.com/how-to-build-a-complex-reporting-dashboard-using-dash-and-plotl-4f4257c18a7f

 

Session 40 & 41

 

Practice

IPython-Dashboard

Live graphs

https://pypi.org/project/IPython-Dashboard/

https://pythonprogramming.net/live-graphs-data-visualization-application-dash-python-tutorial/

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

Session-55,56

 

predicting iris flower species with k-means clustering.

https://medium.com/@belen.sanchez27/predicting-iris-flower-species-with-k-means-clustering-in-python-f6e46806aaee

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

Session-1,2

 

Accessing / collecting the datasets

Lecture Notes 1:- Accessing collecting the datasets-session 1,2

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

Session-3,4

 

Missing values handling

Lecture Notes 1:-  Missing value handling - session 3,4

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

Session-5,6

 

Noise reduction in datasets using python

Lecture Notes 1:-  Noise reduction in datasets using python- session 5,6

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

Session-7,8

 

Finding Correlation between features

Lecture Notes 1:-  Finding Correlation between features- sessions 7,8

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

Session-9,10

 

Outlier elimination

Lecture Notes 1:-  Outlier elimination- sessions 9,10

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

Session-11,12

 

Principal component analysis (PCA)

Lecture Notes 1:-  Principal component analysis (PCA) sessions 11,12

Lecture Notes 2:- PCA for Color image compression (2)

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

Session-13,14

 

Linear discriminant analysis (LDA)

Lecture Notes 1:-  Linear discriminant analysis (LDA)-sessions 13,14

Lecture Notes 2:-  LDA

Lecture Notes 3:-  Linear discriminant analysis (LDA)-sessions 13,14

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)

Lecture Notes 1:- Regression (Linear, Polynomial)- sessions 18,19,20

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

Session-21,22,23

 

Linear Regression Multiple Variables

Lecture Notes 1:-  Linear Regression Multiple Variables-sessions 21,22,23

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

Session-24,25,26

 

Logistic Regression (Multiclass Classification)

Lecture Notes 1:-  Logistic Regression (Multiclass Classification)-sessions 24,25,26

Lecture Notes 2:-  Logistic Regression

Lecture Notes 3:-  Univariate_Logistic_Regression

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

Session-27,28

 

Decision Tree

Lecture Notes 1:-  Decision Tree -sessions 27,28

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

Session-29,30

 

Random Forest

Lecture Notes 1:-  Random Forest-sessions 29,30

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

Session-31,32

 

Artificial Neural Network

Lecture Notes 1:-  Artificial Neural Network - sessions 31,32

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

Session-33,34,35

 

Feed Forward Neural Network

Lecture Notes 1:-  Feed Forward Neural Network - sessions 33,34,35

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

Session-36,37,38

 

Back Propagation Neural Network

Lecture Notes 1:-  Back Propagation Neural Network - sessions 36,37,38

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)

Lecture Notes 1:-  Root Mean Square Error (RMSE)-sessions 42,43

Lecture Notes 2:-  Root Mean Square Error (RMSE)-session-42,43

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

Session-44,45

 

Mean Average Percentage Error (MAPE)

Lecture Notes 1:-  Mean Average Percentage Error (MAPE)-session-44,45

Lecture Notes 2:- Mean Average Percentage Error (MAPE) - sessions 44,45

Lecture Notes 3:-  https://www.youtube.com/watch?v=6GtdvA4Q-zw

https://www.youtube.com/watch?v=Wo5YWXDRXv8

Session-46,47

 

Confusion Matrix (TN, TP, FP, FN)

Lecture Notes 1:-  Confusion Matrix (TN, TP, FP, FN) -sessions 46,47

Lecture Notes 2:-  Confusion Matrix-session-46,47

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

Session-48,49

 

Sensitivity, Specificity, Overall Accuracy

Lecture Notes 1:-  Sensitivity, Specificity, Overall Accuracy-session-48,49

Lecture Notes 2:-  Sensitivity, Specificity, Overall Accuracy -sessions 48,49

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

Session-50,51,52

 

Receiver Operating Characteristic ROC Curve

Lecture Notes 1:-  Receiver Operating Characteristic ROC Curve -sessions 50,51,52

Lecture Notes 2:-  Receiver Operating Characteristic ROC Curve-session-50,51,52

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

Session-53,54,55,56

 

Project review-3(Validation of the model)

ASSIGNMENT:

QUESTION BANK:

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

Session-1,2,3

 

Accessing individual pixels using matrix in image

Lecture Notes :- 4.1 Accessing individual pixels using matrix concept

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

Session-4,5,6

 

Image resize and splitting of image

Lecture Notes :- 4.1 Image resize, grey scale conversion, Colour channel splitting

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)

Lecture Notes :- 4.1-Histogram equalisation (CLACH)

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

Session-18,19,20,21

 

Edge detection (Sobel, Canny)

Lecture Notes :- 4.2 - Edge detection (Sobel, Canny), Morphological operations

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

Lecture Notes :- 4.2-Image segmentation, Image Thresholding, Binary conversion

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

Lecture Notes :- 4.2-Segmentation By clustering

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

Session-39,40,41,42

 

Feature extraction based on size, shape and colour

Lecture Notes :- 4.2-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

Lecture Notes :- 4.2 - A Comparison of SIFT, SURF and ORB on OpenCV

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

Session-47,48,49,50

 

Feature Extraction using convolutional neural network (CNN).

Lecture Notes :-  4.2-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

Lecture Notes :- session-4.3 Matrix flattening, Horizontal stacking, Vertical stacking, padding

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)

Lecture Notes :- session-4.4 Gradient boosting

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

Session-78, 79, 80

 

Multi-layer Perceptron (MLP)

Lecture Notes :- session-4.4 multi-layer perceptron

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

Session-81,82,83

 

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

Lecture Notes 1:- session-4.4 Support vector machine kernels

Lecture Notes 2 :- session-4.4 support vector machine

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-58,59,60,61

 

K-mean clustering in hyperspectral imaging

https://www.neonscience.org/classification-kmeans-pca-python#:~:text=KMeans%20is%20an%20iterative%20clustering,to%20classify%20unsupervised%20data%20(eg.&text=Each%20pixel%20in%20the%20image,pixels%20assigned%20to%20the%20cluster.

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

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

IoT Analytics (0+2+2) -60 hrs

Session- 1

 

Practice 1:

        Creating Things, Certificates, Policies in AWS IoT core Services

Practice 2:

        Connect NodeMCU with AWS IoT Core Services

Practice 3:

        Connect ESP32 with AWS IoT Core Services

Practice 4:

        Connect Raspberry Pi with AWS IoT Core Services

Practice 5:

        Posting Sensor Data to AWS IoT Core Services

Practice 6:

         Controlling Devices from AWS IoT Core Services

Practice 7:

         Storing Sensor Data into DynamoDB using AWS IoT core

Practice 8:

         Get Raspberry Pi to interact with Amazon Web Services & push data into the DynamoDB

Practice 9:

         Posting Sensor Data to the Thingspeak to aggregate, visualize and analyze live data streams in the cloud

Practice 10:

         Portable IoT Based Fingerprint Biometric Attendance System

Practice 11:

         IoT-based Covid Patient Blood Oxygen monitor & calling an ambulance on critical blood oxygen levels

Digital Video Processing (CUML2012) (0-2-1) (45 hrs)

Session -1, 2

Digital Video Acquisition, Principles of Color Video

Notes : https://www.slideshare.net/MazinAlwaaly/multimedia-color-in-image-and-video

Video : https://youtu.be/XZwxyBblkJM

 

 Session -3, 4

 Video Camera, Video Display

Notes : https://www.slideserve.com/shae/digital-video-processing-powerpoint-ppt-presentation

Video: https://www.youtube.com/watch?v=Gnl1vuwjHto

 

Session -5

Analog Vs Digital Video

Notes : Analog Vs Digital Video

 Video : https://youtu.be/Zd2nl5jpn7A

 

Session -6,7

Progressive Vs Interlaced scans

Notes: https://www.slideshare.net/aibad/difference-between-interlaced-progressive-scanning

Video : https://www.youtube.com/watch?v=ZJXczn_jPLQ

 

Session - 8,9

Signal, Bandwidth Characterization of a Digital Video Signal.

Notes : https://www.slideshare.net/devashishraval/introduction-to-video-signals

Video : https://www.youtube.com/watch?v=xAc4TSp87IY

 

Session - 10,11

Spatial and Temporal resolution.

Notes : https://www.slideshare.net/reachquadri/what-is-spatial-resolution

Video : https://www.youtube.com/watch?v=ePp2es3nvDA

Video : https://www.youtube.com/watch?v=zY-Eej5d_lo

 

Session -12

Fourier Analysis of Digital Video Signals

Notes : https://eeweb.engineering.nyu.edu/~yao/EL6123old/FT.pdf

Video : https://www.youtube.com/watch?v=RFOGJB564Kk&list=PLwjK_iyK4LLA-zCHK98LHTek38HUzbOxn

 

Session -13, 14

Temporal Frequency Response and Flicker Perception

Video : https://www.youtube.com/watch?v=E-5p26RfkMo

Notes : https://eeweb.engineering.nyu.edu/~yao/EL6123old/FT.pdf

 

Session -15, 16

Spatial Frequency Response, Spatiotemporal Frequency Response

Notes : https://eeweb.engineering.nyu.edu/~yao/EL6123old/FT.pdf

Video : https://www.youtube.com/watch?v=a-rHXtGLyI0

 

Session -17

Smooth Pursuit Eye Movement

Video : https://www.youtube.com/watch?v=Giv87yVK6XY

Notes : https://www.slideshare.net/AdeWijaya5/smooth-pursuit-eye-movement

 

Session -18

Significance of Video Formatting

Notes :  https://support.microsoft.com/en-us/office/add-format-and-record-video-in-powerpoint-bb7fc99c-71ef-48e3-ac1a-3ebafcd9f3ed

Video :

 

Session -19,20

Data rate and bandwidth trade-off

Notes :

Video :

 

Session -21

File Formats: MP4, MOV, WMV, AVCHD, FLV, AVI, WebM, MKV

Notes : https://www.adobe.com/in/creativecloud/video/discover/best-video-format.html

Video : https://www.youtube.com/watch?v=x6sSfIG8o3U&t=68s

 

Session -22

Digital Video Compression Standards

Notes :

Video :

 

Session -23

Digital Video Compression Metrics

Notes :

Video :

 

Session -24,25

Digital Video Storage Precisions

Notes :

Video :https://www.youtube.com/watch?v=SPvdHvxF--Q

 

Session -26,27

Significance of Video compression

Notes :

Video :https://www.slideshare.net/Shreyash03/video-compression-74491649

 

Session - 28

Video Compression Codec’s: Motion JPEG, JPEG 2000, H.264/MPEG-4 AVC, VP8, HEVC, H.265 High Efficiency Video Codec.

Video : https://www.youtube.com/watch?v=-4NXxY4maYc&t=358s

Notes:

 

Session - 29

Video Editing Basics

Notes : https://www.slideshare.net/TANZICT/video-editing-16421406

Video :

 

Session -30,31

Video Editing Types- Online, Offline, Linear, Non-linear

Notes :

Video :

 

Session -32,33

Assemble, Insert, Rough-cut

Notes :

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

Video: https://www.youtube.com/watch?v=l1C34QmGuIA

 

Session -34, 35

Video Shot Transition Effects: Cut, Fade, Wipe, Dissolve, B-roll

Notes : https://www.masterclass.com/articles/what-is-b-roll-footage-and-how-can-you-use-it#what-is-broll

Video : https://www.youtube.com/watch?v=sgYX1c8rczk

 

Session -36,  37

Video Shot Boundary Detection Methods: pixel differences

Notes : http://ceur-ws.org/Vol-2589/Paper6.pdf

Video : 

 

Session -38,39

Video Shot Boundary Detection Methods:   statistical differences, histogram comparisons

Video :

Notes :http://ceur-ws.org/Vol-2589/Paper6.pdf

http://www.tjprc.org/publishpapers/tjprcfile172.pdf

 

Session -40, 41

Video Shot Boundary Detection Methods: edge differences and motion vectors

Notes :http://www.tjprc.org/publishpapers/tjprcfile172.pdf

Video : 

 

Session -42,  43

Video Shot Detection Performance Metrics: ROC Curves, Recall, Precision, F-Measure

Notes : https://medium.com/swlh/recall-precision-f1-roc-auc-and-everything-542aedf322b9

Video : https://www.youtube.com/watch?v=8d3JbbSj-I8

https://youtu.be/MUCo7NvB9SI

 

 

Mathematics for ML(CUML2009) (2-1-0) (45 hrs)

When Models Meet Data:

Notes :  When Models Meet Data

Session -1

Data, Models, and Learning

Data : https://www.youtube.com/watch?v=vgwyzNkuiN8

Models :  https://www.youtube.com/watch?v=yN7ypxC7838

learning : https://www.youtube.com/watch?v=VCF8kiLtBzU

session-2

Empirical Risk Minimization

video : https://youtu.be/8fsJCyBOizQ

session-3

Parameter Estimation

video : https://youtu.be/6KBIWfGYkd8

Practice-1

Curve Fitting in Python

video : https://www.youtube.com/watch?v=PuNADWh4X5s

session-4

Probabilistic Modelling and Inference

video : https://youtu.be/8qtOzLt0x6U

session-5

Directed Graphical Models

video : https://youtu.be/3XysEf3IQN4

session-6

Model Selection

video : https://youtu.be/iUUSamG4P80

Practice-2

Exploratory Data Analysis in Python

video : https://www.youtube.com/watch?v=-o3AxdVcUtQ

 

Linear Regression:-

Notes :   Linear Regression

session-7

Problem Formulation

video : https://youtu.be/EmFCeFTRVu8

session-8

Parameter Estimation

video : https://youtu.be/6KBIWfGYkd8

session-9

Bayesian Linear Regression

video : https://youtu.be/_pQyco14Qt0

Practice-3

Kernel Density Estimation in Python

video : https://www.youtube.com/watch?v=Wz0QO4bQq6w

session-10

Maximum Likelihood as Orthogonal Projection

video : https://youtu.be/sguol03tfWo

 

Dimensionality Reduction with Principal Component Analysis:-

Notes :  Dimensionality Reduction with Principal Component Analysis

session-11

Problem Setting

Video : https://www.youtube.com/watch?v=wyoS89J3ap4

session-12

Maximum Variance Perspective

video : https://www.youtube.com/watch?v=-A64n7Ss35g

session-13

Projection Perspective

video : https://www.youtube.com/watch?v=vhrJBrQRucw

Practice-4

Probability Distribution Function Plotting in Python

video : https://www.youtube.com/watch?v=fQ0Iy0Sew_U

session-14

Eigenvector Computation and Low-Rank Approximations

video : https://www.youtube.com/watch?v=dfxu9ijDN18

session-15

PCA in High Dimensions

video : https://www.youtube.com/watch?v=s71tHPKgRNo

session-16

Key Steps of PCA in Practice

video : https://www.youtube.com/watch?v=osgqQy9Hr8s

Practice-5

Cumulative Distribution Function Plotting in Python

video : https://www.youtube.com/watch?v=fQ0Iy0Sew_U

session-17

Latent Variable Perspective

video : https://www.youtube.com/watch?v=ztPBVcJic3A

 

Density Estimation with Gaussian Mixture Models:-

Notes : Density Estimation with Gaussian Mixture Models

session-18

Gaussian Mixture Model

video : https://youtu.be/IsJn9b9gmAg

session-19

Parameter Learning via Maximum Likelihood

video : https://www.youtube.com/watch?v=EuyEmNHgskU&t=427s

session-20

EM Algorithm

video : https://youtu.be/qy3WKmSXM64

session-21

Latent-Variable Perspective

video : https://youtu.be/ztPBVcJic3A

Practice-6

Dimensionality Reduction and Feature Extraction in Python

video : https://www.youtube.com/watch?v=h6MCf89GqCM

Classification with Support Vector Machines:-

Notes :  Classification with Support Vector Machines

session-22

Separating Hyperplanes

video : https://www.youtube.com/watch?v=bzIQ6hoHIps

session-23

Primal Support Vector Machine

video : https://youtu.be/OR-xXUmBtYU

session-24

Dual Support Vector Machine

video : https://youtu.be/OR-xXUmBtYU

 

session-25

Kernels

video : https://www.youtube.com/watch?v=9IfT8KXX_9c

session-26

Numerical Solution

video : https://www.youtube.com/watch?v=kEjhbylvk0I

 

ASSIGNMENT:

QUESTION BANK:

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

  1. IoT based Water Management
  2. IoT Disease and Pest Management in Smart Agriculture
  3. Soil Health Monitoring
  4. IoT based Apparel Tracking
  5. Intruder Tracking System

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.