Exam Dates Update (May 16th 2025)
The first exam date is now available in KOS. I will gradually add more dates every week until the end of June. If none of the available dates work for you, feel free to contact me by email to arrange an individual appointment.
13th week (May 13th)
- Student presentations
- Morning session 8:00 - 10:10
- Heart Disease.
- Titanic.
- Scene classification.
- Food classification.
- Afternoon session 12:40 - 13:50
- Simpson and Appartent prices.
- Face Mask Detection / Plant Disease.
- Butterflies.
12th week (May 6th)
- Self-organization:
- Clustering: K-Means, Hierarchical
- Self-organising neural network models: Competitive layer. Self-organising feature maps (SOM).
- lecture12.pdf
- Demonstrations and examples (Hierarchical clustering)
- Demonstrations and examples (K-means Clustering)
11th week (April 29th)
- Convolutional Neural Networks:
- Transfer Learning. Modern Archtectures. Applications.
- Other deep learning models - Brief Introduction
- Self-organization - Introduction, clustering.
- lecture11.pdf (CNN)
- lecture11S.pdf (Self-organization)
- Demonstrations and examples (CNN)
- Demonstrations and examples (Clustering)
10th week (April 22th)
- Convolutional Neural Networks:
- Classic CNN architecture
- Training a CNN fro scratch. Visualizations.
- Efficient processing of image data with data loaders.
- Techniques to iprove generalization. Data Augmentation. Transfer Learning.
- lecture10.pdf
- Demonstrations and examples
9th week (April 15th)
- Multi-layer Neural Networks (MLP – Multilayer Perceptron) – Generalization in MLPs and techniques for preventing overfitting.
- Introduction to Convolutional Neural Networks - Motivation, Convolution operation, Architecture, Practical Examples.
- lecture9_MLP.pdf
- lecture9.pdf
- Demonstrations and examples - MLPs.
- Demonstrations and examples - CNN - Introduction.
- Demonstrations and examples - CNN - Visualizatiions.
8th week (April 8th)
- Multi-layer Neural Networks (MLP – Multilayer Perceptron) – continuation.
- Completing the previous example – MLP in Keras and hyperparameter tuning. TensorBoard.
- More about learning algorithms.
- Examples of solving various tasks using MLP (classification, regression, time series prediction).
- Generalization in MLPs and techniques for preventing overfitting - Introduction.
- lecture8.pdf
- Demonstrations and examples - Binary classification example in Keras.
- Demonstrations and examples - Further examples.
7th week (April 1st)
- Multi-layer Neural Networks (MLP – Multilayer Perceptron) – Introduction to Python libraries for deep neural networks. Installation guide.
- Working with Tensorflow playground
- Analyzing the multilayer neural network model. Step-by-step example using Keras on a sample task (binary classification). Setting hyperparameters and understanding their impact on the training process. Using TensorBoard.
- lecture7.pdf
- Demonstrations and examples - Python libraries for deep learning.
- Demonstrations and examples - Installation guide. Binary classification example in Keras.
6th week (March 25th)
- Single-layer neural network. - Training, practical examples.
- Multilayer neural network (MLP - Multilayer Perceptron) - Introduction, Backpropagation algorithm.
- Introduction to Python libraries for deep learning.
- lecture6.pdf
- Demonstrations and examples - Single-layer neural network.
- Demonstrations and examples - Python libraries for deep learning.
5th Week (March 18th 2025)
- Perceptron with continuous activation function and its training using gradient descent. Examples.
- Single-layer neural network. Introduction. Categorical values.
- lecture5.pdf
- Demonstrations and examples - General perceptron and its training, practical tasks
- Demonstrations and examples - Single-layer neural network.
4th Week (March 11th 2025)
- Perceptron and its learning algorithms. Further examples.
- Linear neuron and its learning algorithms. Examples (demonstrations) and exercises.
- lecture4.pdf
- Demonstrations and examples - Rosenblatt’s algorithm and its variants, practical tasks
- Demonstrations and examples - Linear neuron and its learning, practical tasks
3rd Week (March 4th 2025)
- Perceptron and its learning algorithms. Examples (demonstrations) and exercises.
- lecture3.pdf
- Demonstrations and examples - Rosenblatt’s algorithm and its variants, practical tasks
2nd Week (February 25th 2025)
- From the biological neuron to the artificial neuron
- Perceptron. Geometric interpretation
- Representation of logical functions. Logical threshold circuit.
- Optional (bonus) homework: Design a logical function and the smallest possible perceptron network (logical threshold circuit) for XOR. Alternatively, come up with multiple variations.
- lecture2.pdf
- Example - Demonstration of a perceptron + its geometric interpretation in 2D
- Quick introducion to Python. .
- Example of using numpy and other libraries (matplotlib, pandas). Taken from another course.
- If you are new to Python, please go through our Python Programming Basics course materials https://gitlab.fjfi.cvut.cz/ksi/zpro-2024-public-en
1st Week (February 18th 2025)
- About the Course. Overview. Conditions for Credits.
- Introduction to Machine Learning and Artificial Neural Networks:
- Machine Learning. Types of Tasks. Workflow of a Machine Learning Task.
- History of Artificial Neural Networks.
- lecture1.pdf