Course details

Artificial Intelligence and Machine Learning

SUI Acad. year 2021/2022 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

Deputy Guarantor

Language of instruction

Czech

Completion

Examination (written)

Time span

26 hrs lectures, 13 hrs exercises, 13 hrs projects

Assessment points

60 exam, 20 mid-term test, 20 projects

Department

Lecturer

Instructor

Beneš Karel, Ing. (DCGM FIT BUT)
Fajčík Martin, Ing. (DCGM FIT BUT)
Šůstek Martin, Ing. (DITS FIT BUT)

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.

Why is the course taught

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.

Study literature

  • Lecture materials accessible in WIS
  • C. Bishop: Pattern Recognition and Machine Learning, Springer, 2006 
  • Russell, 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.

Fundamental literature

  • C. Bishop: Pattern Recognition and Machine Learning, Springer, 2006 
  • Russell,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

  1. Introduction to artificial intelligence, machine learning and their relation
  2. State space search, game playing
  3. Basic tasks of machine learning (ML) - detection, classification, regression, prediction, sequence recognition, metrics for quality assessment.  
  4. Basic approaches to ML - decision trees, version spaces, reinforcement learning, active learning. 
  5. Probabilistic approach to classification and recognition - basics of Bayes theory. 
  6. Gaussian model, its interpretation and training, PCA. 
  7. Linear and logistic regression, Support vector machines - basic formulation and kernel trick.  
  8. Neural networks (NN) - basic building blocks, principles of training.
  9. Practical work with deep NNs - mini-batch, normalization, regularization, randomization, data augmentation.  
  10. Sequentional variants of NN: RNN, LSTM, BLSTM, autoencoders, attention models, use of NN embeddings.
  11. Reinforced learning with NNs and without them
  12. Knowledge, reasoning, planning
  13. AI applications 1. 

Syllabus of numerical exercises

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 project is aimed at an artificial intelligence for a given game. A game server, along with additional tools, will be provided to the students. The goal of the project is to create a capable agent combining AI and ML techniques. The project is solved in teams of up to 4 students.

Progress assessment


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

Schedule

DayTypeWeeksRoomStartEndLect.grpGroupsInfo
Thulecturelectures E104 E105 E112 15:0016:50 1MIT 2MIT NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx
Thuexerciselectures E104 E105 E112 17:0017:50 1MIT 2MIT NBIO - NSPE NHPC - NEMB NISY NSEC - NGRI xx

Course inclusion in study plans

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