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.
Supervisor
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
- Introduction to artificial intelligence and concept of agents
- State space search, game playing
- Knowledge, reasoning, planning
- 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.
- AI applications 1.
- AI applications 2.
Fundamentals seminar
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.
Project
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: currently opened course