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Program Overview

Executive M.Tech in Artificial Intelligence at IIT Bhilai is designed for seeking to gain advanced knowledge and skills in Artificial Intelligence (AI). The program combines theoretical concepts with practical applications, enabling participants to design and deploy Al-based solutions for real-world challenges. The curriculum emphasizes machine learning, deep learning, natural language processing, computer vision, and AI ethics while fostering innovation and leadership in AI technologies.

The program is structured to be flexible and interactive, offering a blend of online classes, elective courses, and project-based learning to cater to the needs of working professionals. Graduates will be equipped to pursue roles such as AI researchers, data scientists, and AI consultants.

Eligibility:

Minimum 2 years of relevant experience within the preceding 3 Years*.

Should have B.Tech. / B.E. / B.Sc. in relevant field (minimum 4-year program) / M.S. or M.Sc. in relevant field (minimum 2-year program) / M.C.A. (minimum 2-year program). For the candidates applying based on the M.Sc./M.S./M.C.A. degree, the undergraduate degree must be in a science or engineering field.

In the qualifying degree, at least 55% marks or a CGPA/CPI equivalent to 55% marks. In case the candidate belongs to the SC, ST, or Persons with Disability (PwD) category, this is relaxed to 50% marks or a CGPA/CPI equivalent to 50% marks. To be eligible to apply for equivalency after the third semester, the candidate must have scored the minimum marks in the qualifying degree as specified in the eligibility criteria of the regular M.Tech. program of IIT Bhilai.

*To be computed as per the last day for application submission of this round.

Eligibility for PhD:

Executive M.Tech. degree will be considered eligible for PhD admissions. Other MEQs will remain as per the institute norms.

Program-specific criteria for Ex. M.Tech. admissions

Relevant Experience: Industrial or Teaching experience only.
Relevant field for the qualifying degree:
- A. M.Sc./M.S. (2 years): ME / EE / ECE / CS / MT / PI / AE / IN
- B. B.Sc. (4 years): ME / EE / ECE / CS / MT / PI / AE / IN

Duration:

The nominal duration of the Executive M.Tech. program will be 2 calendar years / 4 semesters (online).

The nominal duration of the Executive M.Tech. program with an equivalency to the regular M.Tech. will be a 5 semesters (3 online, 2 offline). Please contact IIT Bhilai for further details.

Program Fee

(Excluding Optional Fee)

Program Objectives

Acquire a strong foundation in AI principles, methodologies, and tools.

Develop advanced technical skills in AI domains such as machine learning, deep earning, and NLP.

Gain expertise in designing and implementing AI solutions for real-world problems in various industries.

Understand ethical, societal, and governance implications of deploying AI technologies.

Foster innovation and critical thinking to contribute to AI research and advancements.

Build leadership and collaborative skills to lead Al-driven projects and multidisciplinary teams.

Learning Outcomes

Demonstrate proficiency in core AI technologies, including neural networks, computer vision, and NLP.

Analyze and solve complex problems using AI tools and techniques with an emphasis on optimization.

Design, develop, and deploy Al-based systems while ensuring scalability and efficiency.

Adhere to ethical standards and address societal impacts of AI applications.

Effectively communicate AI concepts and solutions to stakeholders and multidisciplinary teams.

Exhibit lifelong learning abilities to stay updated with emerging AI technologies and trends.

Program Highlights

Comprehensive Industry-Aligned Curriculum: Learn the latest in data science and analytics, including machine learning, big data technologies, and predictive modeling, ensuring relevance to modern industry demands.

Flexible Online Learning for Professionals: The fully online mode allows you to balance your work commitments while advancing your expertise, with live sessions and self-paced study options.

Expert Faculty and Industry Mentors: Gain insights from IIT Bhilai’s distinguished faculty and experienced industry professionals, combining academic excellence with real-world expertise.

Prestige: Certification from one of India’s most renowned institutions, boosting your professional credentials.

Career-Focused Outcomes: Equip yourself with in-demand data science skills and earn an IIT Bhilai certification, giving you a competitive edge and unlocking new career opportunities in the data-driven economy.

Virtual Instructor-Led Training: Learn through Virtual Instructor-Led Training (VILT)

WHO IS THIS PROGRAM FOR?

Professionals who are already working in technology-related fields, including IT professionals, software engineers, and AI practitioners—this program provides an opportunity to deepen their expertise.

Entrepreneurs, innovators, tech enthusiasts & engineers eager to master the field of Artificial Intelligence.

Individuals passionate about this field and wanting to stay ahead in the era of intelligent systems and automation.

Engineers and software developers seeking a deep understanding of Artificial Intelligence will find this program invaluable for honing their skills.

Course Curriculum:

Semester I (Total Credits - 10)

Course Code	Course Name	L	T	P	C	Category
AI01	Introduction to Artificial Intelligence	2	1	0	3	Core Course
AI02	Mathematics and Programming for Artificial Intelligence	3	1	0	4	Core Course
AI03	Machine Learning	2	0	2	3	Core Course

Semester II - Total Credits - 9

Course Code	Course Name	L	P	C	Category
AI04	Deep Learning	2	2	3	Core Course
AIEXX	AI Electives-1	3	0	3	Elective 1
AIEXX	AI Electives-1	3	0	3	Elective 2

Semester Ill - Total Credits - 9

Course Code	Course Name	L	T	P	C	Category
AIEXX	AI Electives-2	3	0	0	3	Elective 3
AIEXX	AI Electives-2	3	0	0	3	Elective 4
AIEXX	AI Electives-2	3	0	0	3	Elective 5

Semester IV (Total Credits - 12)

Course Code	Course Name	L	T	P	C	Category
AIP01	Capstone Project	0	0	24	12	MTech Project
OR
AIT01*	Thesis	x	x	x	12	MTech Thesis

Semester - V (Total Credits - 14)

Course Code	Course Name	L	T	P	C	Category
AIT01*	Thesis	x	x	x	14	MTech Thesis

Elective Courses

Category	Sem	Course Code	Course Name	L	C
AI Electives - 1	2	AIE01	Reinforcement Learning	3	3
	2	AIE02	Natural Language Processing	3	3
	2	AIE03	Computer Vision	3	3
	2	AIE04	Systems for AI/ML	3	3
AI Electives - 2	3	AIE04	Learning with Graphs	3	3
	3	AIE05	Distributed AI Systems	3	3
	3	AIE06	Generative AI	3	3
	3	AIE07	Agents and Prompt Engineering	3	3
	3	AIE08	AI for Industry (IoT, Robotics, UAV)	3	3
	3	AIE09	Security and Privacy of AI	3	3

Electives will be floated based on the availability of faculty members. This list may be updated from time to time and change batch-wise.

Additional Notes

*Only for those who have opted for equivalent degree to the regular Mtech program

Students registered only for the Executive MTech (Online) Program: Total Credits : 10 (Semester I) + 9 (Semester II) + 9 (Semester III) + 12 (Semester IV) = 40
Students opted for the equivalent degree to the regular MTech Program: Total Credits : 10 (Semester I) + 9 (Semester II) + 9 (Semester III) + 12 (Semester IV) + 14 (Semester V) = 54

Thesis Requirements:

To achieve equivalency with the regular off-line MTech program, the candidate must complete a total of 26 thesis credit on campus from the fourth semester onwards.

Program Equivalency:

This is an online Executive M.Tech. degree program, which is not equivalent to the regular M.Tech programs of IIT Bhilai. To get a degree equivalent to a regular M.Tech, the student has to opt for 26 credits offline thesis (at IIT Bhilai) after the 3 rd semester. The offline thesis will be for two semesters (semester 4 and semester 5). The selection for the equivalency program will be based on qualifying the mandatory offline written exam and an interaction conducted by IIT Bhilai.

Program Structure

Total Credits: 40

Core Courses: 12-13 Credits

Elective Courses: 13-14 Credits (if the Campus immersion is opted), otherwise 15-16 credits.

Campus Immersion Program: 2 Credits (optional)

Project: 12 Credits (in 4th Semester)

Examination & Evaluation:

End-Semester Offline Exams:

First Semester: At IIT Bhilai Campus.
Second & Third Semesters: At Designated Centers.

The weightage of online evaluation and end-semester exams will be in the proportion of 40:60. To pass a course, a student must attain a minimum of 40% marks in the total marks.

Students will be evaluated in each semester. In addition to continuous online evaluation (Mid semester exam / Quiz / assignment / assessment) in the core/elective courses running in the first three semesters, the end semester exams will be offline* proctored exams. The end-semester offline exams in the first three semesters will preferably be conducted at the IIT Bhilai campus to give the candidates an opportunity to visit and explore the IIT Bhilai campus. This may also be clubbed with other activities such as document verification, campus immersion, project team formation, and interaction with the mentors/supervisors, etc. Exams may be organized at other IIT Bhilai Centers, subject to higher demand.

Examination & Evaluation:

Students will be evaluated in each semester. In addition to continuous online evaluation (quiz/assignment/assessment) in the core/elective courses running in the first three semesters, the end-semester exam will be offline proctored exams.

The first end-semester exam will mandatorily be held at the IIT Bhilai Campus and will be clubbed with document verification. Any student who misses the end - term exam as well as the document verification due to emergency or faces some issues during the document verification must ensure to get the document verification completed during the Compartment/ Improvement Exam of the first semester. If any student fails to complete the document verification even after that exams of the first semester, the student will be terminated from the program.
The end-semester offline exams in the second and third semesters will preferably be conducted at the IIT Bhilai campus to give the candidates an opportunity to visit and explore the IIT Bhilai campus again. This may also be clubbed with other activities such as campus immersion, project team formation, and interaction with the mentors/supervisors, etc. Exams may be organised at other IIT Bhilai Centres (mutually agreed between IIT Bhilai and Edtech partner), subject to higher demand.

Grading Weightage:

The weightage of online evaluation and end-semester exams will be in the proportion of 30:70. To pass a course, a student must attain a minimum of 35% marks in the total marks.

Module :

Foundations of Data Science

Module 1 - Introduction to Data Science

Overview of Data Science and Its Importance
Key Concepts in Data Science: Data Mining, Machine Learning, and Big Data
Data Types and Structures: Quantitative vs. Qualitative Data
Introduction to Data Science Tools and Technologies
The Data Science Process: From Data Collection to Data Cleaning
Ethical Considerations and Data Privacy in Data Science

Module 2 - Introduction to Data Science

Descriptive Statistics: Central Tendency, Variability, and Distributions
Inferential Statistics: Hypothesis Testing and Confidence Intervals
Correlation and Regression Analysis
Non-parametric Statistical Methods
Probability Distributions and Bayesian Thinking
Experimental Design and Analysis

Module 3 - Machine Learning Foundations

Introduction to Machine Learning and Its Applications
Supervised vs. Unsupervised Learning
Key Algorithms: Regression, Classification, Clustering, and Dimensionality Reduction
Model Evaluation and Selection
Overfitting, Underfitting, and Model Optimization
Practical Implementation of Machine Learning Models

Module 4 - Data Visualization and Communication

Principles of Effective Data Visualization
Tools for Data Visualization (e.g., Tableau, Matplotlib, Seaborn)
Interactive Visualizations and Dashboards
Storytelling with Data: Narrative Techniques
Communicating Complex Data Insights to Non-Technical Audiences
Case Studies in Data Visualization

Module 5 - Advanced Topics and Trends in Data Science

Big Data Analytics: Tools and Techniques
Introduction to Deep Learning and Neural Networks
Natural Language Processing and Text Analytics
Time Series Analysis and Forecasting
Ethical AI and Responsible Data Science
Emerging Trends and Future of Data Science

Statistical Methods for Data Analysis

Module 1 - Introduction to Statistics for Data Analysis

Overview of Statistics in Data Analysis
Types of Data: Nominal, Ordinal, Interval, and Ratio
Measures of Central Tendency: Mean, Median, Mode
Measures of Dispersion: Range, Variance, Standard Deviation
Probability Distributions: Normal, Binomial, Poisson
Sampling Techniques and Data Collection

Module 2 - Hypothesis Testing and Inferential Statistics

Concepts of Hypothesis Testing
Types of Errors in Hypothesis Testing
Confidence Intervals
T-tests (One-sample, Independent sample, Paired sample)
Analysis of Variance (ANOVA)
Chi-Square Tests

Module 3 - Regression Analysis

Introduction to Regression Analysis
Simple Linear Regression
Multiple Linear Regression
Diagnostics and Assumptions in Regression Analysis
Non-linear and Logistic Regression
Model Selection and Validation

Module 4 - Multivariate Statistical Techniques

Module 5 - Advanced Topics and Case Studies

Time Series Analysis and Forecasting
Survival Analysis
Bayesian Statistics
Machine Learning in Statistics
Real-world Case Studies and Applications
Ethical Considerations in Statistical Analysis

Big Data Technologies

Module 1 - Introduction to Big Data

Overview of Big Data and its characteristics (Volume, Variety, Velocity, Veracity)
Big Data sources and types
Challenges and opportunities in Big Data
Big Data architecture and ecosystems
Introduction to Hadoop and MapReduce framework
Overview of data storage and management in Big Data (HDFS)

Module 2 - Big Data Processing Technologies

In-depth study of Hadoop ecosystem components (Hive, Pig, HBase)
Real-time processing with Apache Spark
Introduction to NoSQL databases (Cassandra, MongoDB)
Big Data integration and ETL processes
Stream processing technologies (Apache Kafka, Apache Flink)
Cloud-based Big Data solutions (AWS, Azure)

Module 3 - Big Data Analytics

Basics of Big Data analytics and tools
Data mining techniques for Big Data
Machine Learning with Big Data
Advanced analytics: Predictive, prescriptive, and sentiment analysis
Visualization techniques for Big Data
Case studies of Big Data analytics in various sectors

Module 4 - Big Data Security and Privacy

Security challenges in Big Data environments
Data encryption and security tools
Privacy concerns and solutions in Big Data
Regulatory compliance and data governance
Ethical considerations in Big Data analytics
Case studies on Big Data security and privacy

Module 5 - Emerging Trends and Future of Big Data

Internet of Things (IoT) and Big Data
Artificial Intelligence and Big Data
Edge computing in Big Data
Big Data in Industry 4.0
Challenges and future directions in Big Data technologies
Impact of Big Data on society and economy

Data Warehousing and Data Mining

Module 1 - Introduction to Data Warehousing

Overview of Data Warehousing
Data Warehousing Architectures
Data Warehousing Components
Data Modelling for Data Warehouses
ETL Processes: Extraction, Transformation, and Loading
Data Warehouse Performance Optimization

Module 2 - Solar Energy Systems for EVs

Relational Database Design
SQL for Data Warehousing
Data Warehouse vs. Database Systems
Data Quality and Cleaning
Metadata Management in Data Warehouses
Case Studies: Database Systems in Data Warehousing

Module 3 - Introduction to Data Mining

Module 4 - Data Mining Techniques and Algorithms

Classification and Prediction Methods
Clustering Techniques
Association Rule Mining
Decision Trees and Random Forests
Neural Networks and Deep Learning in Data Mining
Time Series Analysis and Mining

Module 5 - Advanced Topics and Applications

Big Data Analytics and Data Mining
Text Mining and Web Mining
Social Network Analysis
Real-Time Data Warehousing and Mining
Trends and Future Directions in Data Mining
Case Studies: Data Mining in Industry

Predictive Analytics

Module 1 - Fundamentals of Predictive Analytics

Introduction to Predictive Analytics: Definition, Scope, and Importance
Data Exploration and Preprocessing: Handling Missing Values, Outliers, and Data Transformation
Statistical Foundations: Probability Theory, Distributions, and Descriptive Statistics
Predictive Modelling Process: Problem Definition, Data Splitting, and Cross-Validation
Overview of Predictive Models: Linear Regression, Logistic Regression, Decision Trees
Model Evaluation Metrics: Accuracy, Precision, Recall, AUC-ROC
Introduction to Software Tools: R, Python, and Specific Libraries (e.g., scikit-learn, pandas)

Module 2: Statistical Methods in Predictive Analytics

Linear and Logistic Regression: Model Building, Assumptions, and Interpretation
Time Series Analysis: Components, ARIMA Models, Seasonality Adjustments
Survival Analysis: Concepts, Kaplan-Meier Estimate, Cox Proportional Hazards Model
Bayesian Statistics: Bayesian Inference, Prior and Posterior Distributions
Dimensionality Reduction Techniques: Principal Component Analysis, Factor Analysis
Model Selection and Validation Techniques: Train-Test Split, K-Fold Cross-Validation

Module 3: Machine Learning for Predictive Analytics

Overview of Machine Learning: Supervised vs. Unsupervised Learning
Classification Algorithms: K-Nearest Neighbors, Support Vector Machines, Naive Bayes
Regression Techniques: Polynomial Regression, Ridge, Lasso
Ensemble Methods: Random Forests, Gradient Boosting Machines
Neural Networks and Deep Learning: Basics, Feedforward Neural Networks
Unsupervised Learning for Prediction: Clustering Techniques, Association Rules
Advanced Topics: Overfitting, Underfitting, Hyperparameter Tuning

Module 4: Applications and Advanced Techniques

Industry Applications: Marketing, Finance, Healthcare, and Supply Chain
Text Analytics and Natural Language Processing: Sentiment Analysis, Topic Modeling
Recommendation Systems: Collaborative Filtering, Content-Based Systems
Deep Learning for Predictive Analytics: Convolutional Neural Networks, Recurrent Neural Networks
Big Data Analytics: Hadoop, Spark, and Their Role in Predictive Modeling
Ethical Considerations and Bias in Predictive Analytics
Future Trends and Innovations in Predictive Analytics

Text Mining and Natural Language Processing

Module 1 - Introduction to Text Mining and Natural Language Processing

Module 2 - Text Representation and Feature Engineering

Module 3 - Machine Learning for Text Mining

Supervised Learning Algorithms in Text Mining
Unsupervised Learning: Clustering Techniques
Semi-supervised and Reinforcement Learning in NLP
Evaluation Metrics for Text Mining Models
Text Classification and Categorization
Sequence Modeling and Prediction

Module 4 - Advanced Topics in NLP

Sentiment Analysis and Opinion Mining
Named Entity Recognition and Entity Linking
Topic Modeling and Latent Dirichlet Allocation
Question Answering and Chatbots
Language Generation and Summarization
Advanced Parsing and Semantic Analysis

Module 5 - Applications and Case Studies

Text Mining in Social Media Analysis
NLP in E-commerce and Customer Service
Text Mining in Healthcare and Biomedical Research
Natural Language Understanding in Intelligent Agents
Ethical Considerations and Bias in NLP
Future Trends and Research Directions in Text Mining and NLP

Time Series Analysis and Forecasting

Module 1 - Introduction to Time Series Analysis

Fundamentals of Time Series Data
Components of Time Series: Trend, Seasonality, Cycle, and Irregular Variations
Stationarity and Differencing
Correlation and Autocorrelation Functions
Introduction to Time Series Decomposition
Basic Visualization Techniques for Time Series

Module 2 - Time Series Modeling

ARIMA Models: Concepts and Applications
Seasonal ARIMA (SARIMA) Models
Exponential Smoothing and Holt-Winters Method
Moving Average and Smoothing Techniques
Non-Stationary Time Series and Unit Root Tests
Model Selection and Diagnostic Checking

Module 3 - Advanced Time Series Models

Vector Autoregression (VAR) Models
State-Space Models and Kalman Filtering
ARCH and GARCH Models for Volatility Forecasting
Multivariate Time Series Analysis
Nonlinear Time Series Models
Introduction to Time Series Analysis in Frequency Domain

Module 4 - Machine Learning in Time Series

Regression Analysis in Time Series
Time Series Classification and Clustering
Neural Networks for Time Series Forecasting
Deep Learning Approaches (LSTM, GRU)
Feature Engineering in Time Series
Model Evaluation and Performance Metrics

Module 5 - Applications and Case Studies

Financial Time Series Analysis and Forecasting
Economic Time Series: GDP, Inflation, and Employment Data
Time Series Analysis in Retail and Sales Forecasting
Energy Demand Forecasting
Time Series in Environmental Science
Case Studies: Real-World Applications

Advanced Statistical Modeling

Module 1 - Foundations of Statistical Modeling

Overview of Statistical Modeling: Concepts and Applications
Probability Distributions and Their Properties
Principles of Estimation: Maximum Likelihood and Bayesian Methods
Hypothesis Testing and Statistical Inference
Linear Regression Models and Assumptions
Model Validation and Diagnostic Techniques

Module 2 - Multivariate Statistical Techniques

Introduction to Multivariate Analysis
Principal Component Analysis (PCA) and Factor Analysis
Discriminant Analysis and Cluster Analysis
Canonical Correlation Analysis
Multivariate Regression Models
Handling Missing Data in Multivariate Analysis

Module 3 - Time Series Analysis and Forecasting

Basics of Time Series Data and Their Characteristics
Autoregressive (AR) and Moving Average (MA) Models
ARIMA and Seasonal ARIMA Models
Time Series Decomposition and Forecasting Techniques
Non-Stationary Models and Cointegration
Case Studies in Time Series Analysis

Module 4 - Bayesian Statistics and Advanced Inference

Introduction to Bayesian Statistics
Bayesian Estimation and Prediction
Hierarchical and Mixed Models
Markov Chain Monte Carlo (MCMC) Methods
Bayesian Model Selection and Averaging
Applications of Bayesian Methods in Real-world Problems

Module 5 - Advanced Topics in Statistical Modeling

Nonparametric and Semiparametric Models
Structural Equation Modeling (SEM)
Generalized Linear Models (GLMs) and Extensions
Machine Learning Techniques in Statistical Modeling
Survival Analysis and Censored Data
Recent Trends and Developments in Statistical Modeling

Energy Storage and Conversion

Module 1 - Introduction to Cloud Computing and Data Analytics

Overview of Cloud Computing: Concepts and Models
Data Analytics in the Cloud: An Introduction
Cloud Service Models: IaaS, PaaS, SaaS
Cloud Deployment Models: Public, Private, Hybrid, and Community
Basic Cloud Architecture and Components
Introduction to Virtualization and its role in Cloud Computing

Module 2 - Cloud Platforms for Data Analytics

Overview of Major Cloud Platforms (AWS, Azure, GCP)
Cloud Storage Options and Database Services
Data Processing Services in the Cloud
Big Data Analytics in the Cloud
Real-time Analytics and Stream Processing
Cloud Security and Compliance in Data Analytics

Module 3 - Building Data Analytics Solutions in the Cloud

Designing Data Pipelines in the Cloud
Data Ingestion, Storage, and Processing Techniques
Implementing ETL (Extract, Transform, Load) Operations
Data Modeling and Warehousing in the Cloud
Introduction to Data Visualization Tools in the Cloud
Best Practices for Scalable Cloud Analytics Solutions

Module 4 - Advanced Analytics and Machine Learning in the Cloud

Machine Learning Services in the Cloud
Implementing Predictive Analytics in the Cloud
Deep Learning and AI Services in Cloud Platforms
Integrating Machine Learning Models with Data Pipelines
Automation and Orchestration of Analytical Workflows
Case Studies: Real-world Applications of Cloud-Based Analytics

Module 5 - Performance Optimization and Future Trends

Performance Tuning for Cloud Analytics Solutions
Cost Management and Optimization in Cloud Analytics
Emerging Trends in Cloud Computing and Data Analytics
Scalability and Reliability in Cloud-Based Analytics
The Future of AI and Analytics in the Cloud
Final Project: Building an End-to-End Cloud Analytics Solution

Deep Learning and Neural Networks

Module 1 - Introduction to Neural Networks

Overview of Artificial Intelligence and Machine Learning
Fundamentals of Neural Networks: Perceptrons and Activation Functions
Feedforward Neural Networks: Architecture and Learning
Backpropagation Algorithm and Gradient Descent
Loss Functions and Optimization Techniques
Practical Implementations and Frameworks Overview

Module 2 - Convolutional Neural Networks (CNNs)

Principles of Convolutional Operations
Architecture of CNNs: Layers and Feature Extraction
Applications of CNNs in Image and Video Processing
Advanced Techniques in CNNs: Pooling, Dropout, and Batch Normalization
Transfer Learning and Pre-trained Models
Case Studies: Image Classification and Object Detection

Module 3 - Recurrent Neural Networks (RNNs) and LSTMs

Introduction to Sequential Data Processing
Architecture of Recurrent Neural Networks
Long Short-Term Memory (LSTM) Networks
Applications in Natural Language Processing and Time Series Analysis
Challenges in Training RNNs: Vanishing and Exploding Gradients
Advanced RNN Variants and Attention Mechanisms

Module 4 - Generative Models and Unsupervised Learning

Overview of Unsupervised Learning in Deep Learning
Autoencoders: Architecture and Applications
Introduction to Generative Adversarial Networks (GANs)
Variational Autoencoders (VAEs)
Applications of Generative Models in Data Augmentation and Synthesis
Challenges and Future Directions in Generative Modeling

Module 5 - Deep Reinforcement Learning and Ethical Considerations

Basics of Reinforcement Learning
Deep Q-Networks (DQNs) and Policy Gradient Methods
Applications of Deep Reinforcement Learning in Games and Robotics
Ethical Implications and Responsible AI in Deep Learning
Future Trends and Research Directions in Deep Learning
Industry Applications and Case Studies

Reinforcement Learning

Module 1 - Introduction to Reinforcement Learning

Overview of Reinforcement Learning
The RL Problem: An Introduction
Elements of Reinforcement Learning
The RL Framework: Markov Decision Processes
Dynamic Programming in RL
Policy and Value Iteration

Module 2 - Model-Free Reinforcement Learning

Basics of Model-Free Prediction
Monte Carlo Methods
Temporal-Difference Learning
TD(λ) and Eligibility Traces
Q-Learning and SARSA
Comparison of Model-Free Algorithms

Module 3 - Value-Based Methods

Basics of Value Function Approximation
Methods of Value Function Approximation
Deep Q-Networks (DQN)
Extensions of DQN (Double DQN, Dueling DQN)
Value-Based Learning in Continuous Spaces
Challenges in Value-Based Learning

Module 4 - Policy-Based Methods

Introduction to Policy Gradients
REINFORCE Algorithm
Actor-Critic Methods
Proximal Policy Optimization (PPO)
Trust Region Policy Optimization (TRPO)
Application of Policy-Based Methods

Module 5 - Advanced Topics and Applications

Partially Observable Markov Decision Processes
Multi-Agent Reinforcement Learning
Inverse Reinforcement Learning
Reinforcement Learning in Real-World Scenarios
Ethical Considerations in RL
Future Trends in Reinforcement Learning

Supervised and Unsupervised Learning Techniques

Module 1 - Introduction to Machine Learning

Overview of Machine Learning and its real-world applications.
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning.
Data Preprocessing: Cleaning, Normalization, and Transformation.
Introduction to Python and relevant libraries for machine learning (NumPy, Pandas, Scikit-learn).
Basic Statistics and Probability Theory in Machine Learning.
Evaluation Metrics for Machine Learning Models.

Module 2 - Supervised Learning Techniques

Linear Regression and Logistic Regression.
Decision Trees and Random Forests.
Support Vector Machines (SVM).
Neural Networks and Deep Learning Basics.
Ensemble Methods and Boosting Algorithms.
Case Studies and Practical Applications of Supervised Learning.

Module 3 - Unsupervised Learning Techniques

Clustering Algorithms: K-Means, Hierarchical, and DBSCAN.
Dimensionality Reduction Techniques: PCA and t-SNE.
Association Rule Learning and Market Basket Analysis.
Anomaly Detection Techniques.
Self-Organizing Maps and Autoencoders.
Case Studies and Practical Applications of Unsupervised Learning.

Module 4 - Advanced Topics and Trends

Introduction to Semi-supervised and Reinforcement Learning.
Deep Learning Advanced Topics: CNNs, RNNs, and GANs.
Big Data and Machine Learning.
Ethical and Societal Implications of Machine Learning.
Model Selection, Optimization, and Hyperparameter Tuning.
Current Research and Future Trends in Machine Learning.

Module 5 - Practical Implementations and Project Work

Data Collection and Preprocessing for Machine Learning Projects.
Implementing Supervised Learning Projects.
Implementing Unsupervised Learning Projects.
Model Evaluation and Optimization Techniques.
Presentation of Projects and Peer Review.
Future Directions and Career Paths in Machine Learning.

Machine Learning at Scale

Module 1 - Introduction to Machine Learning at Scale

Overview of Machine Learning and Big Data
Scalability Challenges in Machine Learning
Distributed Computing Fundamentals
MapReduce and Hadoop Ecosystem
Data Partitioning and Parallel Processing
Introduction to Apache Spark

Module 2 - Scalable Machine Learning Algorithms

Linear Regression at Scale
Large-Scale Classification Techniques
Clustering Algorithms for Big Data
Dimensionality Reduction in High-Volume Data
Ensemble Methods and Random Forests at Scale
Scalable Deep Learning Frameworks

Module 3 - Data Handling and Preprocessing at Scale

Big Data Acquisition and Storage
Data Cleaning and Preprocessing Techniques
Feature Engineering at Scale
Handling Missing and Noisy Data
Stream Processing and Real-time Analytics
Data Security and Privacy Considerations

Module 4 - Advanced Topics in Machine Learning at Scale

Hyperparameter Tuning in Distributed Environments
Scalable Text Mining and Natural Language Processing
Graph Processing and Network Analysis at Scale
Time Series Analysis in Big Data
Recommendation Systems at Scale
Case Studies: Real-world Applications

Module 5 - Tools and Technologies for Machine Learning at Scale

Overview of Big Data Platforms (Hadoop, Spark, Flink)
Machine Learning Libraries (MLlib, TensorFlow, PyTorch)
Cloud-Based Machine Learning Services
Performance Optimization Techniques
Monitoring and Evaluation of Scalable ML Systems
Emerging Trends and Future Directions

Business Intelligence and Analytics

Module 1 - Introduction to Business Intelligence and Analytics

Overview of Business Intelligence and Analytics
Role of Data in Business Decision Making
Introduction to Data Warehousing and Data Mining
Business Analytics and its Types (Descriptive, Predictive, Prescriptive)
Key Performance Indicators (KPIs) and Dashboard Basics
Case Studies in Business Intelligence

Module 2 - Data Warehousing and ETL Processes

Architecture of Data Warehouses
ETL (Extract, Transform, Load) Processes
Data Modeling and Design
Data Warehouse Implementation and Management
Data Governance and Quality
Tools and Techniques in Data Warehousing

Module 3 - Data Mining and Predictive Analytics

Introduction to Data Mining Techniques
Classification, Clustering, and Association Rule Mining
Predictive Modeling and Regression Analysis
Decision Trees and Random Forests
Neural Networks and Machine Learning Basics
Applications of Predictive Analytics in Business

Module 4 - Visualization and Reporting

Principles of Data Visualization
Tools for Data Visualization (e.g., Tableau, Power BI)
Dashboard Design and Best Practices
Interactive Reports and Data Storytelling
Advanced Visualization Techniques
Case Studies in Data Visualization

Module 5 - Advanced Topics and Emerging Trends

Big Data Analytics
Real-time Analytics and IoT Data
Artificial Intelligence in Business Intelligence
Cloud Computing and its Impact on BI
Ethical Considerations and Data Privacy
Future Trends in Business Intelligence and Analytics

Data Governance and Compliance

Module 1 - Introduction to Data Governance

Definition and Scope of Data Governance
Importance of Data Governance in Business
Key Components of Data Governance
Data Governance Frameworks
Roles and Responsibilities in Data Governance
Challenges in Implementing Data Governance

Module 2 - Data Compliance and Regulations

Overview of Data Compliance
Global Data Protection Regulations (GDPR, HIPAA, CCPA, etc.)
Compliance Requirements and Standards
Data Privacy and Security Laws
Role of Compliance in Data Governance
Case Studies on Data Compliance

Module 3 - Implementing Data Governance

Steps in Developing a Data Governance Strategy
Data Quality Management
Data Lifecycle Management
Policies and Procedures in Data Governance
Tools and Technologies for Data Governance
Metrics and KPIs for Data Governance

Module 4 - Risk Management in Data Governance

Identifying and Assessing Data Risks
Risk Management Frameworks
Data Breach and Incident Response
Ethical Considerations in Data Governance
Business Continuity and Data Governance
Case Studies on Risk Management

Module 5 - Advanced Topics in Data Governance

Emerging Trends in Data Governance
Role of AI and Machine Learning in Data Governance
Data Governance in Cloud Computing
Data Governance in Big Data Analytics
Legal and Ethical Implications of Advanced Data Technologies
Future of Data Governance and Compliance

Advanced Topics in Data Science

Module 1 - Big Data Analytics

Introduction to Big Data: Concepts and Challenges
Big Data Technologies: Hadoop, Spark, and NoSQL Databases
Data Mining Techniques for Large Datasets
Advanced Data Preprocessing and Transformation
Real-time Analytics and Stream Processing
Case Studies in Big Data Solutions

Module 2 - Machine Learning at Scale

Overview of Scalable Machine Learning
Distributed Computing for Machine Learning
Advanced Algorithms: Deep Learning, Neural Networks
Implementing Machine Learning Pipelines
Optimization Techniques in Large Scale Learning
Practical Applications of Machine Learning at Scale

Module 3 - Predictive Analytics and Modeling

Principles of Predictive Modeling
Advanced Regression Techniques
Time Series Analysis and Forecasting
Ensemble Methods and Model Stacking
Model Evaluation and Validation
Predictive Analytics in Business and Finance

Module 4 - Advanced Data Visualization and Interpretation

Innovative Data Visualization Techniques
Interactive and Dynamic Visualizations
Advanced Reporting Tools and Dashboards
Visualizing Big Data and High-Dimensional Data
Storytelling with Data
Ethical Considerations in Data Visualization

Module 5 - Emerging Trends in Data Science

Artificial Intelligence and its Role in Data Science
Internet of Things (IoT) Data Analytics
Blockchain Technology in Data Science
Advanced Topics in Data Privacy and Security
Data Science in Healthcare and Genomics
Future Directions in Data Science

Data-Driven Decision Making

Module 1 - Introduction to Data-Driven Decision Making

Overview of Data-Driven Decision Making
Importance of Data in Modern Businesses
Basic Concepts of Data Analytics
Tools and Techniques for Data Analysis
Data Quality and Data Governance
Case Studies in Data-Driven Decision Making

Module 2 - Statistical Methods for Decision Making

Descriptive Statistics in Decision Making
Inferential Statistics and Hypothesis Testing
Regression Analysis for Predictive Insights
Time Series Analysis and Forecasting
Non-parametric Methods in Data Analysis
Application of Statistical Methods in Real-World Scenarios

Module 3 - Data Visualization and Interpretation

Principles of Effective Data Visualization
Tools for Creating Data Visualizations (e.g., Tableau, Power BI)
Interpreting Graphs and Charts for Decision Making
Interactive and Dynamic Data Visualization Techniques
Dashboard Design and Implementation
Case Studies on Effective Data Visualization

Module 4 - Ethical Considerations in Data-Driven Decisions

Understanding Data Privacy and Security
Ethical Implications of Data Collection and Analysis
Regulatory Compliance (e.g., GDPR, HIPAA)
Bias and Fairness in Data Analysis
Ethical Decision Making Frameworks
Case Studies on Ethical Dilemmas in Data Usage

Module 5 - Communicating Data Insights

Techniques for Effective Data Storytelling
Presentation Skills for Data Professionals
Crafting Data-Driven Reports and Proposals
Data-Driven Decision Making in Leadership
Stakeholder Management and Communication Strategies
Real-world Examples of Effective Data Communication

Program Fee :

Executive MTech in Artificial Intelligence
Application Fee (Non Refundable )	5,000
Admission Fee	Instalment 1	Instalment 2	Instalment 3	Instalment 4
Admission Fee	105,000	105,000	105,000	105,000
Total Fee (Excluding Optional Fee)	4,25,000

Optional :

Executive MTech in Artificial Intelligence	Sem 1	Sem 2	Sem 3	Sem 4
Optional Campus Immersion Fee	-	10,000	-	10,000
Optional Institute Alumni Fee	-	-	-	6,000
Total Optional Fee	26,000

Refund Policy:

Click Here to check the refund policy

Admission Process

Step 1

Fill up an online application form, upload the required documents & submit application

Step 2

Make the application payment

Step 3

Shortlisting based on eligibility criteria fulfilled by the applicant

Step 4

If shortlisted, you will receive an offer letter

Step 5

Pay the entire fees on admission confirmation within 7 days of receiving the offer letter

Selection process will be scheduled post-counseling & application process, depending on the number of eligible applications as per seat availability for the program. This entire process will be online.

Campus Immersion :

Program Certificate