REF: | 15651_319005 |
DATE: | 23 - 27 Jun 2025 27.Jun.2025 |
LOCATION: |
Amsterdam (Netherlands) |
INDIVIDUAL FEE: |
5500 Euro |
Introduction:
This advanced training course delves into the intricacies of Artificial Intelligence (AI) and Machine Learning (ML), providing participants with a comprehensive understanding of advanced concepts, techniques, and applications. Designed for experienced professionals in the field, it will cover state-of-the-art developments, research trends, and practical implementations in AI and ML.
Targeted Groups:
- Data Scientists.
- AI and ML Engineers.
- Researchers in AI and ML.
- IT Professionals are seeking advanced knowledge in AI and ML.
- Advanced Computer Science Students.
- Industry Professionals are aiming to implement AI/ML solutions.
Course Objectives:
At the end of this course, the participants will be able to:
- Understand and apply advanced AI and ML algorithms.
- Implement and optimize complex models and architectures.
- Analyze and interpret large datasets using AI and ML techniques.
- Integrate AI and ML solutions into existing systems.
- Stay updated with the latest trends and research in AI and ML.
Targeted Competencies:
- Advanced algorithm design and implementation.
- Data analysis and visualization.
- Model optimization and performance tuning.
- AI/ML integration and deployment.
- Research and development in AI/ML.
Course Content:
Unit 1: Advanced Machine Learning Algorithms:
- Deep Learning Architectures.
- Convolutional Neural Networks (CNNs).
- Recurrent Neural Networks (RNNs).
- Generative Adversarial Networks (GANs).
- 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 Preprocessing Techniques.
- Tokenization.
- Lemmatization and Stemming.
- Advanced NLP Models.
- Transformers (e.g., BERT, GPT).
- Seq2Seq Models.
- Applications of NLP.
- Sentiment Analysis.
- Machine Translation.
- Text Summarization.
Unit 3: Introduction to AI in Accounting and Finance:
- Overview of AI and its evolution in accounting and finance.
- Key concepts and terminologies in AI.
- Importance of AI in modern financial practices.
- Benefits and challenges of AI adoption in finance.
- Current trends and future outlook of AI in the industry.
Unit 4: Model Deployment and Production:
- Model Serving and Monitoring.
- Deploying Models with TensorFlow Serving.
- Monitoring 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 AI and Bias Mitigation.
- Fairness in AI.
- Bias Detection and Correction.
- Legal and Societal Impacts.
- AI Regulations.
- Societal Implications of AI.
- Future Directions and Research.
- Quantum Computing in AI.
- Neuromorphic Computing.