AI specialist with reinforcement learning and design thinking

Introduction to Design Thinking (approx. 1 day)

Design Thinking process at a glance

The most important rules and phases of Design Thinking

Practice-oriented approaches and applications

5 phases in a real project (approx. 3 days)

Research Phase

Methodological input on qualitative research

Implementation through practical exercises on a real project

Synthesis phase

Methodical input on analysis and synthesis

Implementation through practical exercises on a real project

Ideation phase

Methodical input on creative techniques and idea development

Implementation through practical exercises on a real project

Prototyping phase

Methodical input on visualization and prototyping (including mockups, click dummies, 3D printing and rapid prototyping)

Implementation through practical exercises on a real project

Testing phase

Methodical input on test methods and iteration, agile approach

Implementation through practical exercises on a real project

Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment

Project work (approx. 1 day)

To consolidate the content learned

Presentation of the project results

Introduction to Machine Learning (approx. 5 days)

Why machine learning?

Application examples

Supervised learning, unsupervised learning, partially supervised learning, reinforcement learning

Examples of data sets

Getting to know data

Training, validation and test data

Viewing data

Making predictions

Supervised learning (approx. 5 days)

Classification and regression

Generalization, overfitting and underfitting

Size of the data set

Algorithms for supervised learning

Linear models

Bayes classifiers

Decision trees

Random Forest

Gradient Boosting

k-nearest neighbors

Support Vector Machines

Conditional Random Field

Neural Networks and Deep Learning

Probabilities

Unsupervised learning (approx. 5 days)

Types of unsupervised learning

Preprocessing and scaling

Data transformations

Scaling training and test data

Dimension reduction

Feature engineering

Manifold learning

Principal component decomposition (PCA)

Non-negative matrix factorization (NMF)

Manifold learning with t-SNE

Cluster analysis

k-Means clustering

Agglomerative clustering

Hierarchical cluster analysis

DBSCAN

Cluster algorithms

Evaluation and improvement (approx. 2 days)

Model selection and model evaluation

Tuning the hyperparameters of an estimator

Cross-validation

Grid search

Evaluation metrics

Classification

Project work (approx. 3 days)

To consolidate the content learned

Presentation of the project results

Introduction to Deep Learning (approx. 1 day)

Deep learning as a type of machine learning

Fundamentals of neural networks (approx. 4 days)

Perceptron

Calculation of neural networks

Optimization of model parameters, backpropagation

Deep learning libraries

Regression vs. classification

Learning curves, overfitting and regularization

Hyperparameter optimization

Stochastic gradient descent (SGD)

Momentum, Adam Optimizer

Learning rate

Convolutional Neural Network (CNN) (approx. 2 days)

Image classification

Convolutional layers, pooling layers

Reshaping layers, flattening, global-average pooling

CNN architectures ImageNet-Competition

Deep neural networks, vanishing gradients, skip connections, batch normalization

Transfer Learning (approx. 1 day)

Adaptation of models

Unsupervised pre-training

Image data augmentation, explainable AI

Regional CNN (approx. 1 day)

Object localization

Regression problems

Branched neural networks

Methods of creative image generation (approx. 1 day)

Generative Adversarial Networks (GAN)

Deepfakes

Diffusion models

Recurrent neural networks (approx. 2 days)

Sequence analysis

Recurrent layers

Backpropagation through time (BPTT)

Analysis of time series

Exploding and vanishing gradient problems

LSTM (Long Short-Term Memory)

GRU (Gated Recurrent Unit)

Deep RNN

Deep LSTM

Text processing using neural networks (approx. 2 days)

Text preprocessing

Embedding layers

Text classification

Sentiment analysis

Transfer learning in NLP

Translations

Sequence-to-sequence method, encoder-decoder architecture

Language models (approx. 1 day)

BERT, GPT

Attention layers, Transformers

Text generation pipelines

Summarization

chatbots

Deep reinforcement learning (approx. 1 day)

Control of dynamic systems

Agent systems

Training through rewards

Policy Gradients

Deep Q-learning

Bayesian neural networks (approx. 1 day)

Uncertainties in neural networks

Statistical evaluation of forecasts

Confidence, standard deviation

Unbalanced data

Sampling methods

Project work (approx. 3 days)

To consolidate the content learned

Presentation of the project results

Introduction to reinforcement learning (approx. 1 day)

Definition and basic concepts

Differences to other learning methods

Areas of application and examples

Markov Decision Processes (MDPs) (approx. 2 days)

Definition and properties of MDPs

Value functions and policy

Bellman equations

Dynamic Programming Approach

Q-Learning (approx. 2 days)

Definition and algorithm

Exploration vs. exploitation

Convergence and optimization properties

Applications in games, robotics and other areas

Deep reinforcement learning (approx. 3 days)

Deep Q-Learning

Deep Deterministic Policy Gradients (DDPG)

Actor Critical Methods

Policy Gradient Methods

Advanced topics (approx. 4 days)

Model-Based Reinforcement Learning

Multi-Agent Reinforcement Learning

Inverse Reinforcement Learning

Meta Reinforcement Learning

Practical applications (approx. 3 days)

Implementation of reinforcement learning algorithms

Application to selected problems and case studies

Evaluation and tuning of the algorithms

Summary and outlook (approx. 2 days)

Summary of the most important concepts and results

Challenges and future developments in reinforcement learning

Project work (approx. 3 days)

To consolidate the content learned

Presentation of the project results