Course Description :
A.I.D.Ed- Artificial Intelligence of Education
Course Preamble
Intelligent machines have replaced human capabilities in many areas. Artificial intelligence is the intelligence exhibited by machines or software. It is the branches of computer sciences that emphasizes on creating intelligent machines that work and react like humans. Artificial Intelligence spans a wide variety of topics in computer science research, including machine learning, deep learning, reinforcement learning, natural language processing, reasoning, perception etc.
Course Objective
This three month course presents the concepts of Artificial Intelligence and the participants will get to work in the areas of Machine learning, Deep Learning, explore the Platforms for AI, implement methods to solve problems using Artificial Intelligence and Natural Language Processing, etc.
Course Outcome
This course is designed in synchronization with the industry to provide the participants in? Depth knowledge and skills required by Aided fields around the globe. It provides comprehensive knowledge about the fundamental principles, methodologies and industry practices in Aided.
Course Structure
Course Contents
Introduction to Aided and Programming Tools (3 Months)
Introduction to Aided and its applications.
Python: - Basics Data Types, Conditional Statements, Looping, Control Statements, String, List And Dictionary Manipulations, Python Functions, Modules And Packages, Object Oriented Programming in Python, Regular Expressions, Exception Handling.
Introduction to Database Management System & SQL, Database Interaction in Python.
Data Analysis & visualization – using numpy, matplotlib, spicy
R Programming: - Basics - Vectors, Factors, Lists, Matrices, Arrays, Data Frames, Reading data.
Data visualization - bar plot, pie, scatter plot, histogram, scatter matrix
Statistical Analysis -Summary Statistics, Probability distributions in R- Normal distribution, Poisson distribution, Binomial distribution. Correlation and Regression
Machine Learning & Deep Learning (4 Months)
Supervised and Unsupervised Learning, Classification and Regression, Linear Regression, KNN, K Means, Logistic Regression, Support Vector Machines (SVM), Decision Tree, Naïve Bayes, Ensemble Methods, Random Forest, Boosting and Optimization.
Deep Learning Concepts, Basics of Artificial Neural Network, Deep Neural Networks, Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Tensor flow, Keras, Introduction to Generative Adversarial Networks(GAN),Open CV
Natural Language Processing (1 Month)
Basics of text processing, Lexical processing, Syntax and Semantics, Other problems in text analytics
AI Platforms & Reinforcement Learning (1 Months)
Introduction to Aided /Cognitive platforms and Understand the basics of Reinforcement Learning and its applications in Aided
Mini Project (3 Months)
Eligibility
BE/B.tech/B.Sc/Graduate (IT/Computer Science/Electronics), BCA, 3 year Diploma (IT/Computer Science/Electronics), Degree holders with PGDCA, DOEACC A, B level Or equivalent of any of these having good computer programming knowledge.
Final year students have to include the copies of course completion certificate of their qualifying degree/ diploma or copies of the mark lists up to the last semester/ year. On the date of counseling/ admission, he/she must produce the originals of course completion certificate/ mark lists up to the last semester/year examination.
Course Fees
General Candidates: Course fee is Rs.25,000.00 + GST @ 18%
SC/ST Candidates: Tuition Fees/Examination fees are for SC/ST students admitted under SCSP/TSP. However they are required to remit an amount of Rs. 3500.00 as Advance caution/security deposit. This amount will be considered as caution/security deposit and will be refunded after successful completion of the course. If the student fails to complete the course successfully this amount along with any other caution/security deposits by the student will be forfeited.
