Machine and deep learning with MATLAB

Introduction to Machine Learning and the ML Workflow (approx. 3 days)

Fundamentals, Applications, and Types of Machine Learning

Classification of Modern AI and Machine Learning Methods

Machine Learning Workflow with scikit-learn

Training, validation, and test data

Underfitting, overfitting, and model evaluation

Cross-Validation

Data Preparation and Feature Engineering (approx. 2 days)

Data Quality and Preprocessing

Scaling, Normalization, and Encoding

Feature Engineering and Feature Selection

Handling Missing Values, Outliers, and Imbalanced Datasets

Supervised Learning – Regression and Classification (approx. 5 days)

Regression and Classification Methods

Linear and Regularized Models

Distance-, probability-, and tree-based methods

Ensemble and Boosting Methods

Model Comparison and Hyperparameter Optimization

Evaluation Metrics and Probabilistic Predictions

Feature importance and explainable AI 

Unsupervised Learning (approx. 2 days)

Dimension Reduction and Feature Extraction

Clustering Methods and Evaluation of Clustering Results

Anomaly Detection

Time Series Analysis and Forecasting (approx. 1 day)

Fundamentals and Analysis of Time Series Data

Forecasting Methods and Machine Learning for Time Series

Neural Networks and Modern AI (approx. 2 days)

Fundamentals of Neural Networks and Deep Learning

Machine Learning for Text Data and Natural Language Processing

Foundation Models, Large Language Models, and Generative AI

Deployment of ML models (approx. 1 day)

Storage and deployment of trained models

Development of simple ML applications

Interfaces and platforms for machine learning models

Project work (approx. 4 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)

Multilayer perceptrons

Calculation of neural networks

Optimization of model parameters, backpropagation

Deep learning libraries

Regression vs. classification

Typical loss and activation functions

Evaluating the model prediction with metrics

Regression and classification metrics

Learning curves, overfitting and regularization

Hyperparameter optimization

L1/12 regularization

Dropout

Early stopping

Stochastic gradient descent (SGD)

Momentum, Adam Optimizer

Optimization of the learning rate

Dynamic learning rate adjustment

Reduce Learning Rate on Plateau

Learning rate optimization with the TensorBoard

Controlling the fit process with callbacks

Saving and loading models

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)

Adapting and combining models

Unsupervised pre-training

Image data augmentation, explainable AI

Data loader

Regional CNN (approx. 1 day)

Object localization

Semantic segmentation

Regression problems

Branched neural networks

YOLO architecture

U-Net models

Methods of creative image generation (approx. 1 day)

Generative Adversarial Networks (GAN)

Deepfakes

Diffusion models

Superresolution

Supplementing image areas

Apply foundation models from Hugging Face

Multimodal models

LoRA-Fine-Tuning

Application areas of generative models

Legal restrictions

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

Natural Language Processing (NLP)

Translations

Text generation

Sequence-to-sequence method, encoder-decoder architecture

Encoder-only and decoder-only models

Local application of large language models

Language models (approx. 1 day)

Transfomer architecture

Attention and Multihead-Attention

Positional Encodings

Fine-tuning large language models

Prompting

Text generation pipelines

Summarization

Chatbots

Retrieval Augmented Generation

AI agents

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

MATLAB basics (approx. 2 days)

MATLAB user interface

Reading data from a file

Variables, arrays, operators, basic functions

Graphical representation of data

Customizing diagrams

Exporting graphics

Variables and commands (approx. 2 days)

Relational and logical operators

Sets, sets with 2D solids (polyshape)

Performing mathematical and statistical calculations with vectors

Graphics in statistics

Analysis and visualization (approx. 1 day)

Creating and modifying matrices

Mathematical operations with matrices

Graphical representation of matrix data

Matrix applications: Mappings, rotation, systems of linear equations, least square method

Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment

Data processing (approx. 1 day)

Data types: Structure arrays, cell arrays, string vs. char, categorical, datetime and much more.

Creating and organizing tabular data

Conditional data selection

Importing/exporting with Matlab: folder structures, .mat data, table data, continuous texts

MATLAB programming (approx. 3 days)

Control structures: loops, if-else, exceptions

Functions

Object-oriented programming

App design

Simulation in MATLAB (approx. 5 days)

Numerical integration and differentiation

Basics of simulation of ordinary differential equations, Matlab ODE and solver options

Simulation technology in Matlab: input parameters, data interpolation, simulation studies

Simulation control: event functions (zero crossing), output functions

Application examples, e.g. simulation of an electric motor, simulation of a rocket

Simulink (approx. 4 days)

Basics of Simulink: Diagrams, functions, signals and differential equations

Functions, subsystems and libraries

Import/export, lookup tables, control

Zero-crossing, automation of simulation tasks (Matlab access)

Application examples, e.g. simulation of an aircraft drive train

Project work (approx. 2 days)

To consolidate the content learned

Presentation of the project results