Pattern Recognition Training in Copenhagen, Denmark

The Pattern Recognition in AI training delves into the techniques and methodologies used to identify patterns and make data-driven predictions in artificial intelligence. Participants will explore core concepts such as feature extraction, classification, clustering, and statistical modeling, along with practical applications in fields like image processing, speech recognition, and anomaly detection. This training emphasizes hands-on learning, equipping participants with the skills to design and implement effective pattern recognition systems using modern AI tools and frameworks.

Introduction to Pattern Recognition and Basic Techniques

1. Introduction to Pattern Recognition
• Overview of pattern recognition and real-world applications
• Pattern recognition workflow: Data preprocessing, feature extraction, and modeling
• Practical exercise
2. Feature Extraction and Engineering
• Importance of feature engineering in pattern recognition
• Techniques: Scaling, normalization, and polynomial features
• Practical exercise
3. Dimensionality Reduction Techniques
• Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA)
• Feature selection and subset selection methods
• Practical exercise
4. Introduction to Supervised Learning
• Overview of classification algorithms: k-Nearest Neighbors (k-NN), Naïve Bayes, and Decision Trees
• Model evaluation metrics: Accuracy, precision, recall, and F1-score
• Practical exercise

Advanced Techniques and Real-World Applications

5. Unsupervised Learning and Clustering
• Introduction to clustering techniques: k-Means and Hierarchical Clustering
• Evaluating clustering performance: Silhouette score and Davies-Bouldin index
• Practical exercise
6. Advanced Classification Techniques
• Support Vector Machines (SVM) and Neural Networks for pattern recognition
• Overfitting and regularization: L1, L2, and dropout techniques
• Practical exercise
7. Real-World Applications of Pattern Recognition
• Pattern recognition in image processing: OCR and facial recognition
• Pattern recognition in time-series data: Stock market analysis
• Practical exercise