8. Synopsis
**Introduction**

1. Introduction, description of the subject requirements. Overview of the
areas of artificial intelligence, its application in embedded systems and the
focus of the subject.

**Information processing in embedded AI systems**

2. Description of the data
analysis workflow. Outlier detection and data cleaning, handling missing data,
exploring the possibilities of knowledge modeling.

3. Analysis of the problems
and solutions of regression and classification in a hardware environment,
introduction to the related linear and logistic models.

4. Examination of the
clustering problem, study of dimensionality reduction options.

5. Introduction to artificial
intelligence sensor fusion methods for embedded applications.

6. Introduction to neural
networks. Demonstration the effect of noise on the learning process. Examining
the problems of overlearning, early stopping and backtracking on different
platforms. Decomposition of the sample set into training, test and validation
sets.

7. Description of the
functionality of convolutional neural networks. Presentation of a pattern
recognition system that can be run in an embedded environment.

8. Study of feedback neural
networks. Introduction to the possibilities of prediction.

9. Interpreting the output of
neurons. Demonstration of the importance of representation learning,
description of autoencoder.

**Embedded platforms for artificial intelligence applications**

10. Overview of application
limitations of general purpose devices (microcontroller, FPGA, general purpose
processor).

11. Presentation of target
hardware for implementing artificial intelligence on embedded platforms.

12. Presentation of smart
devices, smart watches capabilities for embedded AI.

**Detailed topics of the exercises**

1. Application of linear and
logistic regression and classification in an embedded environment, using
examples with known physical models, testing the representational capabilities
of linear models, adding new variables to the model.

2. Challenges of high
dimensionality data, removing linear dependencies, applying principal component
analysis and singular value decomposition to dimensionality reduction on an
embedded platform, quantifying information loss, testing reversibility.

3. Sensor data integration,
noise management, measurements from different sources, fusion of different
measurement methods in hardware implementation.

4. Implementing applied neural
networks in embedded systems, investigating the impact of noise on learning,
calculating confidence of convergence, and discussing coupled over-learning,
early shutdown and backtracking challenges. Decomposition of samples into
training, test and validation sets.

5. Embedded application
examples of convolutional neural networks, impact of kernel sizes on
representability, explanatory analysis of learned feature vectors.

6. Time-series data analysis
on embedded platforms, comparative analysis of autoregressive (ARIMA) methods
and feedback neural network-based prediction architectures.

7. Unsupervised feature vector
learning, the impact of latent dimensionality on the representativeness of
models, sampling of generative models.