Intelligent systems

Knowledge of basic statistics and programming.

Lecture topics:
1. Introduction to intelligent systems and data science
2. Nature inspired computing (genetic algorithms, genetic programming)
3. Introduction to predictive modelling
4. Bias, variance and overfitting
5. Representation learning and feature selection
6. Ensemble methods
7. Kernel methods
8. Neural networks: architectures, backpropagation, deep neural networks
9. Model inference and explanation
10. Natural language processing: text representation, information extraction, text classification, semantic similarity
11. Reinforcement learning: basic approaches and algorithms, Q learning, TD learning, deep RL

G. James, D. Witten, T. Hastie, and R. Tibshirani, 2021. An introduction to statistical learning with applications in R, 2nd edition. New York: Springer.
D. Jurafsky, J. H. Martin. Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. 3rd edition draft, 2022
Richard S. Sutton and Andrew G. Barto: Reinforcement Learning, An Introduction, 2nd edition, MIT press, 2018

The goal of the course is the students to become acquainted with the field of intelligent systems, which includes a collection of tools and approaches for solving problems which are difficult or unpractical to tackle with other methods. Students will be able to apply the gained theoretical knowledge on real-world problems from scientific and business environment. The students shall be able to decide which of the presented techniques should be used for a given problem, and to develop a prototype solution.

General competences:
the ability to understand and solve professional challenges in computer and information science,
the ability of professional communication in the native language as well as a foreign language,
the ability to apply acquired knowledge in independent work for solving technical and scientific problems in computer and information science,
familiarity with research methods in the field of computer science.

Subject-specific competences:
using basic machine learning algorithms
preprocessing data for data mining
feature subset selection
evaluation of decision models
using data mining systems
using optimizations packages with evolutionary techniques
text analysis and text mining
using reinforcement learning tools

Upon course completion the student will:
know and use various techniques and methods for modelling of intelligent systems
know and use machine learning tools
know and use text analysis approaches
solve and analyse examples of intelligent systems using scientific methods
use and evaluate tools for statistical modelling and data mining
be capable to analyse problems from the area of intelligent systems and choose adequate approaches for their solutions
use and compare different approaches for evolutionary computing

Lectures, assignments with written and oral demonstrations and presentations, seminar works and homework. Students from small project teams and autonomously solve assignments based on real-life problems. The teams describe their solutions in written reports and prepare short oral presentations. Written reports and oral presentations are graded.

Continuing: homework, project work.
Final: written and oral exam.
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

VREŠ, Domen, ROBNIK ŠIKONJA, Marko. Preventing deception with explanation methods using focused sampling. Data mining and knowledge discovery. 2023, vol. , no. , str. 1-46
MIOK, Kristian, ŠKRLJ, Blaž, ZAHARIE, Daniela, ROBNIK ŠIKONJA, Marko. To BAN or not to BAN: Bayesian attention networks for reliable hate speech detection. Cognitive computation. Jan. 2022, vol. 14, iss. 1, str. 353-371
ŠKVORC, Tadej, GANTAR, Polona, ROBNIK ŠIKONJA, Marko. MICE: mining idioms with contextual embeddings. Knowledge-based systems. Jan. 2022, vol. 235, str. 1-11
MARTINC, Matej, POLLAK, Senja, ROBNIK ŠIKONJA, Marko. Supervised and unsupervised neural approaches to text readability. Computational linguistics. 2021, vol. 47, no. 1, str. 141-179
LAVRAČ, Nada, ŠKRLJ, Blaž, ROBNIK ŠIKONJA, Marko. Propositionalization and embeddings: two sides of the same coin. Machine learning. 2020, vol. 109, no. 7, str. 1465-1507.

Celotna bibliografija je dostopna na SICRISu https://cris.cobiss.net/ecris/si/sl/researcher/8741
Complete bibliography is available in SICRIS: https://cris.cobiss.net/ecris/si/en/researcher/8741