Artificial Intelligence and Machine Learning

• 26 hrs lectures
• 13 hrs exercises
• 13 hrs projects

• 60 pts final exam (written part)
• 20 pts mid-term test (written part)
• 20 pts projects

Make students acquainted with the basics of artificial intelligence (AI) and machine learning (ML) that are the basic components of modern scientific methods, industrial systems and end-user applications - for example self-driving cars, cognitive robotics, recommendation systems, recognition of objects in images, chat-bots and many others. Show traditional techniques linked to currently dominating deep neural networks. Introduce basic mathematical formalism of AI and ML, that can be developed in specialized courses. Give an overview of software tools for AI and ML.

Artificial intelligence (AI) and machine learning (ML) are nowadays spread from expected places (Google, Facebook) to recommendation systems in e-shops, games, search for travel itineraries, camera focusing, and many others. SUI course provides a basic overview of algorithms and applications of AI and ML for all students of the master program at FIT, and thus fulfills the "AI" in its title. After SUI, artificial neural networks and other components of ML and AI system wont be "magical black boxes" anymore. You will know the basis they are build around - for many of you, this information will be sufficient; those interested can go deeper in specialized courses.

• http://www.fit.vut.cz/study/courses/SUI/public/prednasky
• C. Bishop: Pattern Recognition and Machine Learning, Springer, 2006
• Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7
• Ertel, W.: Introduction to Artificial Intelligence, Springer, second edition 2017, ISSN 1863-7310
• Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016.

1. Introduction to artificial intelligence and concept of agents
2. State space search, game playing
3. Knowledge, reasoning, planning
4. Basic tasks of machine learning (ML) - detection, classification, regression, prediction, sequence recognition, metrics for quality assessment.  
5. Basic approaches to ML - decision trees, version spaces, reinforcement learning, active learning. 
6. Probabilistic approach to classification and recognition - basics of Bayes theory. 
7. Gaussian model, its interpretation and training, PCA. 
8. Linear and logistic regression, Support vector machines - basic formulation and kernel trick.  
9. Neural networks (NN) - basic building blocks, principles of training.
10. Practical work with deep NNs - mini-batch, normalization, regularization, randomization, data augmentation.  
11. Sequentional variants of NN: RNN, LSTM, BLSTM, autoencoders, attention models, use of NN embeddings. 
12. AI applications 1. 
13. AI applications 2.

Lectures will be immediately followed by demonstration exercises (1h weekly) where examples on data and real code in Python will be presented. Code and data of all demonstrations will be made available to the students.

The project is solved in teams of up to 3 students and its assignment will be announced during the semester.

• Half-semestral exam (20pts)  
• Submission of project (20pts) 
• Semestral exam, 60pts, requirement of min. 17pts.