Lectures
1. Introduction to Artificial Intelligence, Types of Learning
2. Regression Tasks and Linear Regression
3. Decision and Regression Trees
4. Cluster Analysis
5. Support Vector Machines
6. Basics of Neural Networks
7. Training and Optimization of Neural Networks
8. Deep Neural Networks
9. Large Language Models
10. Reinforcement Learning
11. Biologically Inspired Algorithms
12. Implementation of Machine Learning into Computationally Limited Systems
13. Trends in Artificial Intelligence
Laboratories
1. Basics of Python for Data Analysis, Working with Libraries, Data Visualization
2. Implementation of Linear Regression, Model Training and Result Evaluation
3. Creating a Decision Tree, Tree Visualization and Data Analysis
4. Application of the K-means Algorithm, Visualization and Cluster Analysis
5. Training SVM Model, Hyperparameter Optimization
6. Creating, Training and Testing a Neural Network
7. Neural Network Parameters and Tracking Metrics
8. Application of CNN and RNN
9. Practical Implementation of GPT
10. Implementation of Reinforcement Learning
11. Implementation of Evolutionary Algorithm and Optimization Task
12.-13. Project Solution and Evaluation
1. Introduction to Artificial Intelligence, Types of Learning
2. Regression Tasks and Linear Regression
3. Decision and Regression Trees
4. Cluster Analysis
5. Support Vector Machines
6. Basics of Neural Networks
7. Training and Optimization of Neural Networks
8. Deep Neural Networks
9. Large Language Models
10. Reinforcement Learning
11. Biologically Inspired Algorithms
12. Implementation of Machine Learning into Computationally Limited Systems
13. Trends in Artificial Intelligence
Laboratories
1. Basics of Python for Data Analysis, Working with Libraries, Data Visualization
2. Implementation of Linear Regression, Model Training and Result Evaluation
3. Creating a Decision Tree, Tree Visualization and Data Analysis
4. Application of the K-means Algorithm, Visualization and Cluster Analysis
5. Training SVM Model, Hyperparameter Optimization
6. Creating, Training and Testing a Neural Network
7. Neural Network Parameters and Tracking Metrics
8. Application of CNN and RNN
9. Practical Implementation of GPT
10. Implementation of Reinforcement Learning
11. Implementation of Evolutionary Algorithm and Optimization Task
12.-13. Project Solution and Evaluation