Introduction to Artificial Intelligence in Basic Sciences

Course Overview:

The course allows attendants to become familiar with the essentials of artificial intelligence nature and methods in order to facilitate their understanding of procedures and current results. It also pursues to provide the necessary background for developing their own applications with available tools. Especial emphasis is made on machine learning techniques for treating databases, including the so‐called “big data” referring to data sets that are too large or complex to be dealt with by traditional data‐processing application software. It is intended for scientists familiar with basic computational sciences and advanced undergraduate students in Chemistry, Physics, Biology, Pharmacy and Biochemistry.

Lecture content outline:

Information and systems. Computer systems. Boolean algebra. Intelligence. Artificial Intelligence. Intelligent agents. Percept sequence, performance measure, rational agents, task environments. Learning agents. Agent architecture and programing. Expert systems. Machine learning. Machine learning applications. Modeling, classification, regression, clustering. Data mining. Bayes theorem and applications. Bayesian networks. Naïve Bayes. Learning by Bayesian networks. Creating and teaching a machine. Machine learning paradigms: supervised, unsupervised and reinforced learning. Machine learning algorithms. Genetic algorithms as optimization processes. Artificial neural networks and “deep” learning. Machine learning applications. Data representations. “Inverse” machine learning. Some current relevant applications in science and shortcomings. Computational tools available.

Main References:

Some books serve as consulting material like:

1. Russell, S.; Norvig, P., Artificial Intelligence. A Modern Approach. 4th. Global ed.; Pearson Education Limited: Harlow, 2022.

2. Xu, Y.; Liu, X.; Cao, X.; Huang, C.; Liu, E.; Qian, S.; Liu, X.; Wu, Y.; Dong, F.; Qiu, C.‐W.; Qiu, J.; Hua, K.; Su, W.; Wu, J.; Xu, H.; Han, Y.; Fu, C.; Yin, Z.; Liu, M.; Roepman, R.; Dietmann, S.; Virta, M.; Kengara, F.; Zhang, Z.; Zhang, L.; Zhao, T.; Dai, J.; Yang, J.; Lan, L.; Luo, M.; Liu, Z.; An, T.; Zhang, B.; He, X.; Cong, S.; Liu, X.; Zhang, W.; Lewis, J. P.; Tiedje, J. M.; Wang, Q.; An, Z.; Wang, F.; Zhang, L.; Huang, T.; Lu, C.; Cai, Z.; Wang, F.; Zhang, J., Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2021, 2 (4), 100179.

3. Kirkpatrick, J.; McMorrow, B.; Turban, D. H. P.; Gaunt, A. L.; Spencer, J. S.; Matthews, A. G. D. G.; Obika, A.; Thiry, L.; Fortunato, M.; Pfau, D.; Castellanos, L. R.; Petersen, S.; Nelson, A. W. R.; Kohli, P.; Mori‐Sánchez, P.; Hassabis, D.; Cohen, A. J., Pushing the frontiers of density functionals by solving the fractional electron problem SCIENCE 2021, 374, 1385–1389.

4. Artificial Intelligence Index Report. Chapter 1: Research and Development; Stanford University: 2021.

5. Hoffmann, R.; Malrieu, J.‐P., Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part A. Stage Setting. Angewandte Chemie International Edition in English 2020, 59.

6. Nguyen, G.; Dlugolinsky, S.; Bobák, M.; Tran, V.; López García, Á.; Heredia, I.; Malík, P.; Hluchý, L., Machine Learning and Deep Learning frameworks and libraries for large‐scale data mining: a survey. Artificial Intelligence Review 2019, 52 (1), 77‐124.

7. Tabor, D. P.; Roch, L. M.; Saikin, S. K.; Kreisbeck, C.; Sheberla, D.; Montoya, J. H.; Dwaraknath, S.; Aykol, M.; Ortiz, C.; Tribukait, H.; Amador‐Bedolla, C.; Brabec, C. J.; Maruyama, B.; Persson, K. A.; Aspuru‐Guzik, A., Accelerating the discovery of materials for clean energy in the era of smart automation. Nature Reviews Materials 2018, 3 (5), 5‐20.

8. Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A., Machine learning for molecular and materials science. Nature 2018, 559 (7715), 547‐555.

9. Ertel, W., Introduction to Artificial Intelligence. 2nd. ed.; Springer: Cham, 2011.

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