Date & Time: 21-27 October 2024 at 19:00-21:00 IST

Registration Fee: 250 INR / 10 USD

Venue: Online mode

About the Workshop

The “Next-Gen AI and ML: An International Forum on Breakthroughs and Innovations” is a one-week global event dedicated to exploring cutting-edge developments in Artificial Intelligence (AI) and Machine Learning (ML). This workshop brings together leading researchers, industry pioneers, and thought leaders to delve into the latest breakthroughs, future trends, and practical applications of AI and ML across various sectors. Through an immersive series of expert talks, hands-on sessions, and interactive discussions, participants will gain deep insights into the current state of AI, emerging trends, and future directions. Special focus will be given to interdisciplinary applications of AI and ML, from healthcare to energy, education, and beyond. The forum will also offer a unique opportunity for participants to collaborate on AI-driven projects, network with experts, and explore real world applications that drive innovation across industries.

Key Highlights Global Perspectives: Learn from AI and ML experts worldwide, sharing knowledge on the latest developments and real-world application. Objectives Provide a platform for discussing the latest breakthroughs in AI and ML. Facilitate knowledge exchange between academia, industry, and the startup ecosystem. Showcase emerging technologies and interdisciplinary applications of AI and ML. Address ethical concerns and promote responsible AI practices. Foster collaboration and innovation through project work and networking.

Who Should Attend?

• AI and ML Professionals: Researchers, engineers, and developers seeking to stay updated on AI and ML advancements.
• Academics and Students: Scholars, PhD candidates, and students interested in interdisciplinary AI applications and research.
• Industry Leaders: Business professionals looking to leverage AI and ML innovations for their organizations.
• Startups and Innovators: Entrepreneurs and startups aiming to scale AI-driven solutions.
• Policy Makers and Ethicists: Individuals interested in understanding the social, ethical, and regulatory implications of AI technologies.

Objectives

• Provide a platform for discussing the latest breakthroughs in AI and ML.
• Facilitate knowledge exchange between academia, industry, and the startup ecosystem.
• Showcase emerging technologies and interdisciplinary applications of AI and ML.
• Address ethical concerns and promote responsible AI practices.
• Foster collaboration and innovation through project work and networking.

Key Highlights Global Perspectives

• Global Perspectives: Learn from AI and ML experts worldwide, sharing knowledge on the latest developments and real-world applications.
• Interdisciplinary Focus: Explore how AI and ML are transforming fields like healthcare, energy, education, and social impact.
• Hands-on Experience: Participate in workshops and live demonstrations showcasing cutting-edge AI tools and technologies.
• Ethics and Responsibility: Discuss critical issues surrounding AI ethics, bias, and responsible AI development.
• Networking Opportunities: Engage with academic researchers, industry professionals, and startup founders through panels, discussions, and networking events.

PATRON(S)

Prof. M. P. Poonia: Campus Director, Baba Farid Group of Institutions, Bathinda, INDIA

Prof. Mahesh M. Bundele: Principal & Director, Poornima College of Engineering, Jaipur, INDIA

CO-PATRON(S)

Dr. Manish Bansal: Principal, Baba Farid College, Bathinda, INDIA

Dr. Pankaj Dhemla: Professor & Vice-Principal, Poornima College of Engineering, Jaipur, INDIA

Ms. Kgomotso B Morotolo: Nexus University, SOUTH AFRICA

Prof. Sonia Malik: Dean of R&D, BFGI, Bathinda, INDIA

CONVENER(S)

Dr. Mehar Chand: Department of Mathematics, BFC, Bathinda, INDIA & President of MTTF

Prof. Shilpi Jain: Department of Mathematics, Poornima College of Engineering, Jaipur, INDIA

COORDINATOR(S)

Dr. Amanjot Kaur: Department of Physical and Mathematical Sciences, BFC, Bathinda, INDIA

Dr Nthape Mphasha: Faculty of Materials Engineering, Nexus University, SOUTH AFRICA

Dr. Pooja Sharma: ADMIRE TEAM, Baba Farid College, Bathinda, INDIA

Er. Sourabh Kumar: Assistant VP, MathTech Thinking Foundation, Fazilka, INDIA

Dr Taulu:   Faculty of Agricultural Sciences, Nexus University, SOUTH AFRICA

Content

Day 1: AI and ML – Current Trends and Future Prospects Session Title: The Future of AI and ML in Research and Industry Hour 1: Keynote and Overview

• Introduction to AI/ML Innovations: A comprehensive overview of current breakthroughs in AI and ML, focusing on recent advancements in deep learning, reinforcement learning, and transfer learning.
• Future Trends: A forward-looking discussion on trends like explainable AI, federated learning, and the role of AI in real-world problem solving.
• Industry Perspectives: Insights on how leading organizations are adopting AI to solve complex industry challenges (e.g., automation, healthcare, finance).

Hour 2: Panel Discussion – The Role of AI in Revolutionizing Industries Expert Panel: Academics and industry leaders will explore the impact of AI in various industries.

• How AI is reshaping automation, finance, and supply chain management.
• Discussion on the adoption of AI in emerging economies.
• Q&A: Audience can engage directly with experts on industry-specific questions.

Day 2: Advanced Machine Learning – Techniques and Applications

Session Title: Mastering Machine Learning – From Algorithms to Real-World Solutions

Hour 1: Lecture – Key ML Algorithms and Techniques

• Supervised and Unsupervised Learning: Explore core algorithms like decision trees, support vector machines, k-nearest neighbors, and clustering algorithms.
• Optimization Techniques: Discuss gradient descent, hyperparameter tuning, and regularization strategies.
• Real-World Applications: Demonstrate case studies where these algorithms have been applied to solve business and research problems.

Hour 2: Hands-on Lab

• Implementation of ML Algorithms: Step-by-step walkthrough on implementing supervised ML models in Python (e.g., using scikit-learn or TensorFlow).
• Practical Problem-Solving: Participants will work through real-world datasets (e.g., customer segmentation, predictive maintenance) and apply algorithms learned.

Day 3: Deep Learning and Neural Networks

Session Title: Unpacking Deep Learning – From Theory to Practice Hour 1: Overview of Neural Networks

• Introduction to Neural Network Architecture: Deep dive into perceptron’s, feedforward networks, backpropagation, and activation functions.
• Convolutional and Recurrent Neural Networks: Learn how CNNs are applied in computer vision, and RNNs/LSTMs in sequence modeling (e.g., text, time-series).
• AI Model Interpretability: Discussion on explainability of neural network models and interpretability challenges in black-box AI systems.

Hour 2: Hands-on Lab – Building and Training Deep Neural Networks

• Deep Learning with TensorFlow/Keras: Participants will build a simple neural network model for image classification or sentiment analysis.
• Model      Training     and     Optimization:      Cover      training,     validation, hyperparameter tuning, and techniques to avoid overfitting.
• Q&A Session: Focused on resolving implementation challenges.

Day 4: AI Ethics, Bias, and Fairness

Session Title: Responsible AI – Ethics, Bias, and Fairness in AI Models

Hour 1: Lecture – Ethics in AI Understanding Bias: Explore examples of AI bias in areas like facial recognition, healthcare, and hiring processes.

• Fairness in AI Systems: Discuss how to build and audit fair AI models, touching on fairness-aware algorithms and debiasing techniques.
• Ethical Frameworks: Introduction to frameworks like the EU’s AI regulation and IEEE’s Ethics in AI standards.

Hour 2: Case Study and Panel Discussion

• Case Study: Real-life examples of biased AI models and the societal impact of these biases.
• Panel Discussion: Experts in AI ethics, law, and policy will debate regulatory challenges, responsible AI deployment, and frameworks for ethical AI.
• Interactive Q&A: Open discussion for participants to raise concerns and share views.

Day 5: AI in Emerging Technologies

Session Title: Exploring AI in Quantum Computing and Emerging Fields

Hour 1: Lecture – AI and Quantum Computing Synergies

Session Title: Advanced Optimization Methods in AI
Hour 1: Overview of Optimization in AI

• Introduction to Optimization Techniques in AI: The role of optimization in
  training AI models, fine-tuning algorithms, and enhancing the performance
  of AI systems.
• Gradient-Based Optimization:
• Gradient Descent and Its Variants: Discuss standard gradient
  descent, stochastic gradient descent (SGD), mini-batch gradient
  descent, and their applications in neural networks and machine
  learning.
• Advanced Techniques: Momentum, RMSProp, and the widely-used
  Adam optimizer for faster convergence.
• Evolutionary Algorithms (EA):
• Genetic Algorithms (GA): A look at how genetic algorithms can solve
  optimization problems when objective functions are complex or nondifferentiable.
• Particle Swarm Optimization (PSO): Exploring swarm intelligence for
  optimizing nonlinear, multi-modal problems in AI.

Hour 2: Practical Applications and Demonstrations
Hyperparameter Optimization:

• Grid Search, Random Search, and Bayesian Optimization: How these
  techniques are used to optimize machine learning models.
• Hyperband and Early Stopping: Efficient strategies to tune models
  while minimizing computational cost.
  Real-World Case Studies:
• Case studies in optimizing deep learning networks, AI-based control
  systems, and resource-constrained environments.
• Multi-Objective Optimization: Using Pareto optimization to balance
  trade-offs (e.g., accuracy vs. computational cost).
• Interactive Q&A and Discussion: Open floor for participants to ask
  questions about specific optimization techniques and their applications.

Day 7: Collaborative AI Projects and Closing Ceremony Session Title: Project Presentations and Workshop Closing

Hour 1: Collaborative Project Showcase

• Team Presentations: Participants will present the AI projects they have worked on during the week (e.g., predictive modeling, AI solutions for real- world problems).
• Feedback from Experts: Experts will provide constructive feedback on the projects and suggest ways to improve or scale the solutions.

Hour 2: Closing Ceremony

• Closing Panel Discussion: Summing up the week’s discussions, highlighting key takeaways, and discussing the future of AI and ML.

Farewell Remarks: Final remarks and invitations for future collaboration among participants

Jointly Organized by

MathTech Thinking Foundation, Fazilka, INDIA
Poornima College of Engineering, Jaipur, INDIA
Baba Farid College, Bathinda, INDIA
Nexus University, SOUTH AFRICA