Course detail

Artificial Intelligence and Machine Learning

FIT-SUIAcad. year: 2019/2020

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.

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

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.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

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

Language of instruction

Czech

Work placements

Not applicable.

Aims

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.

Classification of course in study plans

  • Programme MITAI Master's

    specialization NGRI , any year of study, winter semester, 5 credits, compulsory
    specialization NSEC , any year of study, winter semester, 5 credits, compulsory
    specialization NEMB , any year of study, winter semester, 5 credits, compulsory
    specialization NHPC , any year of study, winter semester, 5 credits, compulsory
    specialization NISY , any year of study, winter semester, 5 credits, compulsory
    specialization NMAT , any year of study, winter semester, 5 credits, compulsory
    specialization NVER , any year of study, winter semester, 5 credits, compulsory
    specialization NADE , 1. year of study, winter semester, 5 credits, compulsory
    specialization NBIO , 1. year of study, winter semester, 5 credits, compulsory
    specialization NNET , 1. year of study, winter semester, 5 credits, compulsory
    specialization NVIZ , 1. year of study, winter semester, 5 credits, compulsory
    specialization NCPS , 1. year of study, winter semester, 5 credits, compulsory
    specialization NISD , 1. year of study, winter semester, 5 credits, compulsory
    specialization NIDE , 1. year of study, winter semester, 5 credits, compulsory
    specialization NMAL , 1. year of study, winter semester, 5 credits, compulsory
    specialization NSEN , 1. year of study, winter semester, 5 credits, compulsory
    specialization NSPE , 1. year of study, winter semester, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

  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.

Exercise

13 hours, compulsory

Teacher / Lecturer

Syllabus

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.

Projects

13 hours, compulsory

Teacher / Lecturer

Syllabus

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

eLearning

eLearning: opened course