The knowledge and skills required to design and implement in Python "intelligent", effective, and efficient solutions to problems for which a direct solution is either impractical or unknown. In more details: Knowledge and understanding * Explain basic concepts of CI * Account for the historical development, current situation, and future prospects for some sub-area of CI * Compare advantages and disadvantages of basic CI algorithms * Choose/evaluate pros and cons of alternatives knowledge representations Skills and abilities * Choose appropriate algorithms for solving given problems in a memory- and time-efficient manner * Implement from scratch single-state and simple population-based methods in Python * Tweak and exploit existing libraries * Design multi-agent systems Judgement and approach * Summarize and constructively criticize scientific texts.

* Some programming experience with Python 3 (a crash course on advanced topics is included) * Minimal experience with GitHub (e.g., communicating through issues and pull requests) * Suggested: Algorithms and Programming * Not required: mathematics and statistics * Not required: knowledge of specific toolkits (e.g., Jenetics, scikit-learn, Open BEAGLE, PyTorch, Keras/TensorFlow)

Introduction (10h) * What is "Computational Intelligence"? (included: what is "Artificial Intelligence"? Weak AI vs. Strong AI; the Turing Test; ...) * Symbolic vs. Sub-symbolic intelligence * Solving problems by searching; trial n'error vs. learning vs. evolution * Metaheuristics (exact vs. approximate, ad-hoc heuristics) * Evolutionary Computation (bio-inspired methodologies, natural selection) Single-State Methods (10h) * Hill-climbing, simulated annealing, iterated local search, variable neighborhood search * Simple Evolution Strategies: (1+1), (1+λ) and (1,λ) Population Methods (15h) * Unified approach to Evolutionary Algorithms * Parameter optimization (Evolution Strategies, Differential Evolution) * Symbolic regression (Genetic Programming) * Swarm intelligence (Ant Colony Optimization, Particle Swarm Optimization) * Memetic Algorithms (hybridization) * Model fitting (Estimation of Distribution Algorithm) * Multi-objective optimization Representation problems; genotype space and operators (5h) * Knowledge representation * Trivial (bit strings, integer, real numbers); Permutations; Graphs * Fuzzification Policy optimization (10h) * Reinforcement learning, Q-Learning (not included: Deep Q-Learning) * Rule-based systems and Learning Classifier Systems Multi agent systems (10h) * Artificial Immune System and Learning Classifier System * Simple agents * Learning agents * Games (Adversarial Search)

In order to meet coursework deadlines, students that cannot attend in presence are required to partecipate online or to watch the recorded lecture within 24h.

* 42 hours lectures * 18 hours labs

Textbook * None (lectures and on-line material will be sufficient) Recommended Books * A. E. Eiben, J. E. Smith; "Introduction to Evolutionary Computing" (2nd edition) * S. Luke; "Essentials of Metaheuristics" (2nd edition) * M. Mitchell; "Artificial Intelligence: A Guide for Thinking Humans" * S. Russell, P. Norvig; "Artificial Intelligence: A Modern Approach" (4th edition) * B. Slatkin; "Effective Python: 90 Specific Ways to Write Better Python" (2nd edition)

Lecture slides; Lab exercises; Video lectures (current year);

Exam: Compulsory oral exam; Individual project;

Students need to complete a research project tackling a specific problem; they will need to skim the scientific literature and then implement an appropriate methodology or develop a new one. Students are encouraged to work in teams, but for the sake of the exam each one will be individually responsible for the whole assignment. The oral exam consists in a discussion starting from the research project and then covering all topics of the course (max 30/30). Up to 6 additional points will be granted to students showing an active and timely participation, that is, doing labs, presenting their works in class, and peer-reviewing colleagues' labs. If the total is >31, then the final vote is 30L. Students must produce a final report detailing their activities and share the access of a repository on Github containing the code al least 1 week (168h) before the oral exam.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.