Two-Day Course Overview
Artificial intelligence (AI) and machine learning have long since found their way into large parts of the industry. Success has most recently been achieved in autonomous driving, medical image processing or material testing.
In the future, AI will play an even greater role in numerous industries and scenarios. To remain fit for this future, companies need to deal with the basics of AI and machine learning.
Foundations Training Topics
This training is very practice-oriented. Half of it consists of applied exercises on a consistent topic. You will learn the relevant methods and practices around machine learning while focusing on artificial neural networks, the basis for deep learning.
- Introduction and definition of Terms.
- Use-Cases and Shortcomings of AI.
- Presentation of Technologies Used In The Workshop, e.g., TensorFlow and Keras.
- Example architectures.
Landscape of AI Methods
- Machine Learning methods, e.g., Supervised, Unsupervised and Reinforcement Learning.
- Traditional AI Methods, e.g., Heuristic-Based Techniques and Symbolic AI.
- Advanced AI Methods, e.g., Self-Supervised Learning and Transfer Learning.
Single-Layer Neuronal Networks (Perceptrons)
- Biological Motivation.
- From Biological to Artificial Neurons.
- Learning: Optimization and Gradient Descent.
- Classification of Multiple Classes.
Basic Terms and Tools
- Loss Functions.
- Performance Metrics.
- Data Partitioning.
- Feature Extraction, Dimensionality Reduction
- Overfitting and Countermeasures.
Multi-layer Neural Networks
- Deep Learning.
Convolutional Neural Networks
- Introduction to Convolution.
- Convolutional Neural Networks for Image-Based Problems.
Optional UL-CAIP Exam
Participants who complete all two days of training are eligible to take a two hour certification exam on the morning of the third day. Those who pass the exam are individually certified as a UL Certified Artificial Intelligence Professional – Foundations or UL-CAIP.