Course details
Artificial Intelligence and Machine Learning
SUI Acad. year 2024/2025 Winter semester 5 credits
Overview of methods for solving AI tasks, including game playing. Logic and its use in task solving and planning. PROLOG vs. AI. Basic tasks of machine learning, metrics for quality assessment. Basic approaches to ML - decision trees, version spaces, reinforcement learning, active learning. Probabilistic approach to classification and recognition, Gaussian model, its interpretation and training. Linear and logistic regression. Support vector machines. Neural networks (NN) - basic building blocks, principles of training. Practical work with "deep" NNs. Sequential variants of NN. AI applications.
Guarantor
Course coordinator
Language of instruction
Completion
Time span
- 26 hrs lectures
- 13 hrs seminar
- 13 hrs projects
Assessment points
- 60 pts final exam (written part)
- 20 pts mid-term test (written part)
- 20 pts projects
Department
Lecturer
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Dočekal Martin, Ing. (DCGM)
Hradiš Michal, Ing., Ph.D. (DCGM)
Instructor
Learning objectives
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.
Students will:
- get familliar with basic nomenclature of machine learning, esp. of modern neural networks
- understand the relation between a task, a model and the process of learning
- review classical search-based methods of artificial intelligence and will see the possibilities of combining them with machine learning
- get familliar with basic machine learning models (gaussian models, gaussian classifiers, linear regression, logistic regression)
- get familiar with modern neural networks for solving different tasks (classification, regression, tasks in reinforcement learning scenarios) on various kinds of data (unstructured, image, text, audio) and with methods of their training
Study literature
- Materiály k přednáškám dostupné v Moodlu
- 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.
Syllabus of lectures
- Introduction to artificial intelligence, machine learning and their relation
- Basic tasks of machine learning (ML) - detection, classification, regression, prediction, sequence recognition, metrics for quality assessment.
- Basic approaches to ML - decision trees, version spaces, reinforcement learning, active learning.
- Probabilistic approach to classification and recognition - basics of Bayes theory.
- Gaussian model, its interpretation and training, PCA.
- Linear and logistic regression, Support vector machines - basic formulation and kernel trick.
- Neural networks (NN) - basic building blocks, principles of training.
- Practical work with deep NNs - mini-batch, normalization, regularization, randomization, data augmentation.
- Sequentional variants of NN: RNN, LSTM, BLSTM, autoencoders, attention models, use of NN embeddings.
- Reinforced learning with NNs and without them
- State space search, game playing
- Local search, constraint satisfaction problems
- AI applications.
Syllabus of seminars
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.
Syllabus - others, projects and individual work of students
The subject includes three homework assignments:
- Data modelling and simple classifiers
- Construction of a simple neural network
- Problem solving by search
Progress assessment
- Half-semestral exam (20pts)
- Three homework assignments (20pts)
- Semestral exam, 60pts, requirement of min. 20pts.
Schedule
Day | Type | Weeks | Room | Start | End | Capacity | Lect.grp | Groups | Info |
---|---|---|---|---|---|---|---|---|---|
Wed | lecture | 1., 6., 7., 8. of lectures | E104 E105 E112 | 16:00 | 17:50 | 294 | 1MIT 2MIT | NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx | Hradiš |
Wed | lecture | 2., 3., 4., 5. of lectures | E104 E105 E112 | 16:00 | 17:50 | 294 | 1MIT 2MIT | NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx | Burget |
Wed | lecture | 10., 11., 12. of lectures | E104 E105 E112 | 16:00 | 17:50 | 294 | 1MIT 2MIT | NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx | Beneš |
Wed | lecture | 2024-11-13 | D0206 | 16:00 | 17:50 | 294 | 1MIT 2MIT | NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx | Hradiš |
Wed | lecture | 2024-12-11 | E104 E105 E112 | 16:00 | 17:50 | 294 | 1MIT 2MIT | NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx | Dočekal |
Wed | seminar | 1., 10., 11., 12., 13. of lectures | E104 E105 E112 | 18:00 | 18:50 | 294 | 1MIT 2MIT | NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx | Beneš |
Wed | seminar | 2., 3., 4., 5. of lectures | E104 E105 E112 | 18:00 | 18:50 | 294 | 1MIT 2MIT | NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx | Burget |
Wed | seminar | 6., 7., 8. of lectures | E104 E105 E112 | 18:00 | 18:50 | 294 | 1MIT 2MIT | NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx | Hradiš |
Wed | seminar | 2024-11-13 | D0206 | 18:00 | 18:50 | 294 | 1MIT 2MIT | NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx | Hradiš |
Course inclusion in study plans