Advanced Concepts in Artificial Intelligence (AI) and Machine Learning (ML)

Introduction:

This training course delves into advanced Artificial Intelligence (AI) and Machine Learning (ML) concepts, tools, and methodologies. Participants will become proficient in AI and ML technologies, understand their benefits, and address practical challenges. Aimed at experienced professionals, the course covers cutting-edge innovations, research directions, and practical applications in AI and ML.

The training emphasizes developing complex forecasting models using AI and ML tools, including natural language processing (NLP). Participants will explore AI and ML applications in accounting and finance, and discuss model deployment, as well as legal and ethical considerations.

For those interested, the course provides information on placements and additional details for an AI and ML master’s certificate program.

Objectives:

By the end of this course, participants will be able to:

• Accurately elaborate on advanced AI and ML issues.
• Construct and enhance sophisticated models and structures.
• Utilize AI and ML for analyzing large datasets.
• Integrate acquired skills and solutions into existing systems.
• Stay updated with emerging trends in AI and ML.

Training Methodology:

• Case studies
• Simulations
• Hands-on projects
• Group discussions

Course Outline:

Unit 1: Advanced Machine Learning Algorithms

• Introduction to Deep Learning
• Convolutional Neural Networks
• Recurrent Neural Networks
• Generative Adversarial Networks
• Ensemble Methods
• Bagging and Boosting
• Random Forests
• Gradient Boosting Machines
• Reinforcement Learning
• Q-Learning
• Policy Gradients
• Deep Reinforcement Learning

Unit 2: Natural Language Processing (NLP)

• Text Preparation
• Tokenization
• Lemmatization and Stemming
• Recent Developments in NLP
• Transformer Models (e.g., BERT, GPT)
• Seq2Seq Architecture
• Domain-Based Applications of NLP
• Sentiment Analysis
• Translation through Computers
• Text Compression

Unit 3: AI in Accounting and Finance

• Introduction to AI in Accounting and Finance
• Fundamentals and Key Terms in AI
• Relevance of AI to Financial Operations
• Opportunities and Challenges in AI for Finance
• Status and Forecast for AI in the Industry

Unit 4: Model Deployment and Production

• Model Serving and Monitoring
• Model Deployment with TensorFlow Serving
• Assessment of Model Performance
• Scalability and Performance
• Distributed Computing with Spark
• Parallel Processing Techniques
• AI/ML in the Cloud
• AWS SageMaker
• Google AI Platform

Unit 5: Ethics and Future Trends in AI/ML

• Ethical and Bias Considerations in AI
• Justice in AI
• Bias Identification and Intervention
• Challenges and Issues in AI
• AI Policies
• Social Impact of AI
• Future Trends and Research
• AI’s Role in Quantum Computing
• Neuromorphic Computing