Artificial Intelligence (AI) and Machine Learning Training
Course Overview
Artificial Intelligence (AI) and Machine Learning is a must have skill in the 21st century. The amount of data produced around the world is growing rapidly, enabling exciting opportunities for enhanced innovation and impact through machine learning and artificial intelligence (AI). However, without the right strategies and frameworks for collecting, storing, and analyzing available data, many organizations are leaving value on the table—and failing to keep pace with the competition and also failing to adopt well informed strategic decisions.
In this prestigious certificate program will acquire hands-on skills crucial for the growth of your organization and also relevant to your personal goals, equipping you with the cutting-edge strategies.
Expected Learning Outcomes
• Acquire proven strategies for maximizing the value of your data
• Learn how to formulate problems as machine learning tasks and identify the right tools for each challenge
• Anticipate and mitigate scaling issues, including data volume, dimensionality, storage,
• Deepen your understanding of the many opportunities, costs, and likely performance hurdles
in predictive modeling and computation
• Explore cutting-edge areas of machine learning and AI, such as deep learning, computer vision and reinforcement learning
• Access industry-specific insights across a variety of areas
Target Audience
The professional certificate Program in Machine Learning & Artificial Intelligence is designed for learners with professional experience. Professionals who will find the curriculum helpful include:
• Data scientists and other analytics professionals
• Developers, software engineers, and programmers
• Executives and managing directors
• Managers involved in making informed strategic decisions
• Statisticians, applied mathematicians, and similar professionals
• Technical managers and team leaders
• Any technical professional whose work interfaces with data analysis
Learning Modules
Foundations of Artificial Intelligence
• Causality for Artificial Intelligence and Machine Learning
• Deep Learning: Architectures & Methods
• Introduction to Artificial Intelligence
• Probabilistic Graphical Models
• Reinforcement Learning: From Foundations to Deep Approaches
• Statistical Relational Artificial Intelligence: Logic, Probability, and Computation
• Statistical Machine Learning
AI Models and Methods.
• Data Mining and Machine Learning
• Deep Learning
• Information Theory I: Fundaments
• Continual Machine Learning
• Robot Learning
• Model Checking
• Optimization of static and dynamic systems
• Optimization Algorithms.
• Deep Generative Models.
AI Systems
• Advanced Data Management Systems
• Analysis of Hybrid Systems
• Automated Theorem Proving
• Concepts and Technologies for Distributed Systems and Big Data Processing.
• Scalable Data Management Systems.
AI Domains and Applications
• 3DScanning & Motion Capture
• AmbientIntelligence
• Bioinformatics
• Capturing Reality
• Computer Vision I
• Ethics in Natural Language Processing
• Foundations of Language Technology
• Foundations of Robotics
• Intelligent Robotic Manipulation Advanced topics in Robot Perception, Planning and Control
• Learning and Educational Technologies
• Human and Identity centric Machine Learning
• Model Predictive Control and Machine Learning
• Natural Language Processing and the Web
• Technology transfer and entrepreneurship with a focus on artificial intelligence
Other Key Modules
• Machine Learning for Big Data and Text Processing: Foundations
• Machine Learning for Big Data and Text Processing: Advanced
• Advanced Data Analytics
• Reinforcement Learning
• AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment
• Deep Learning for AI and Computer Vision
• Designing Efficient Deep Learning Systems
• Ethics of AI: Safeguarding Humanity
Way forward After the Training
Participants will develop a work plan through the help of facilitators that stipulates application of skills acquired in improving their organizations. ASPM will monitor implementation progress after the training.
Training Evaluation:
Participants will undertake a simple assessment before the training to gauge knowledge and skills and another assessment will be done after the training in-order to demonstrate knowledge gained through the training.
