Machine Learning

Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves.

Topics to be Covered in Workshop 

Basics of AI & Introduction  

  • Artificial Intelligence
  • Environmental Constraints
  • Various Agent Types
  • PEAS Analysis of Problem
  • CSP – Introduction
  • Process flow for an AI agent
  • Machine Learning Introduction
  • Supervised & Unsupervised Learning
  • Regression & Classification Problems 

Fuzzy Logic 

  • Getting started with Fuzzy Logic
  • Applications of Fuzzy Logic
  • Problem Formulation, Defuzzification&Rulebase
  • Membership Functions
  • Defuzzification Methods
  • Mamdani&Sugeno Methods
  • Washing Machine Problem
  • Tipping Problem Analysis
  • Fuzzy Logic packages in Python
  • Using Pyfuzzy with python
  • Programming Fuzzy Logic Applications
  • Practical Examples, Case Studies & Hands on session on Fuzzy Logic

Linear Regression 

  • Regression Problem Analysis
  • Mathematical modelling of Regression Model
  • Gradient Descent Algorithm
  • Programming Process Flow
  • Use cases
  • Programming Using python
  • Building simple Univariate Linear Regression Model
  • Multivariate Regression Model
  • Boston Housing Prizes Prediction
  • Cancer Detection Predictive Analysis
  • Best Fit Line and Linear Regression

Decision Trees 

  • Forming a Decision Tree
  • Components of Decision Tree
  • Mathematics of Decision Tree
  • Decision Tree Evaluation
  • Practical Examples & Case Study
  • Random Forest 

Artificial Neural Networks 

  • Neurons, ANN & Working 
  • Single Layer Perceptron Model
  • Multilayer Neural Network
  • Feed Forward Neural Network
  • Cost Function Formation 
  • Applying Gradient Descent Algorithm
  • Backpropagation Algorithm & Mathematical Modelling
  • Programming Flow for backpropagation algorithm
  • Use Cases of ANN
  • Programming SLNN using Python 
  • Programming MLNN using Python
  • Digit Recognition using MLNN
  • XOR Logic using MLNN & Backpropagation
  • Diabetes Data Predictive Analysis using ANN
  • Project – Banking Problem Analysis – When the customer will leave?
  • Project – Medical Problem Analysis

Support Vector Machine 

  • Concept and Working Principle
  • Mathematical Modelling
  • Optimization Function Formation
  • The Kernel Method and Nonlinear Hyperplanes
  • Use Cases
  • Programming SVM using Python 
  • Character recognition using SVM
  • Regression problem using SVM
  • Wisconsin Cancer Detection using SVM

Image Processing with Opencv 

  • Image Acquisition and manipulation using opencv
  • Video Processing
  • Edge Detection
  • Corner Detection
  • Face Detection
  • Image Scaling for ANN
  • Training ANN with Images
  • Character Recognition 

Clustering 

  • Hierarchical Clustering
  • K Means Clustering
  • Use Cases for K Means Clustering
  • Programming for K Means using Python
  • Image Color Quantization using K Means Clustering Technique

Deep Learning Networks

Introduction to Tensor Flow

  • The Programming Model
  • Data Model
  • Tensor Board
  • Introducing Feed Forward Neural Nets
  • Softmax Classifier
  • ReLU Classifier
  • Dropout Optimization
  • Deep Learning Applications

Convolutional Neural Networks 

  • CNN Architecture 
  • Pooling 
  • Variants of the Basic Convolution Function 
  • Efficient Convolution Algorithms 
  • The Neuro-scientific Basis for Convolution Networks

Natural Language Processing 

  • Natural Language Processing & Generation
  • Semantic Analysis
  • Syntactic Analysis
  • Language Translation
  • Using NLTK
  • Using Textblob
  • Sentiment Analysis
  • Project: Streaming live tweets and Sentiment Analysis

Advice For applying Machine Learning

Machine Learning for System Design

Python Libraries used:

  • Numpy
  • Matplotlib
  • Pandas
  • Theano
  • Scikit-learn
  • Opencv
  • TensorFlow
  • Keras
  • Scikit-Image
  • Keras
  • Quandl
  • NLTK
  • Textblob

Eligibility: Computer Science (CS), Information Technology (IT) Engineering Branch, M.Tech, MCA, BCA Students. Students entering into 2nd Year to Final Year Students can participate in this training Program. However students from any branch can participate in this training program.

Certification Policy:

  • Certificate of Merit for all the workshop participants from Innovians Technologies.
  • Certificate of Coordination for the coordinators of the campus workshops

Duration: 5 Days - The duration of this workshop will be five consecutive days, with 6-7 hour session each day.

Fees: Rs. 2500/- (inclusive of all Taxes) per participant  (Min 40 Participants).

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