This course is an introduction to artificial intelligence. It covers the fundamentals principles and methods for developing artificial intelligence and machine learning systems and presents applications to mechanical engineering problems.

The introduction to artificial intelligence course covers the following modules:

• Introduction to Artificial Intelligence (AI) and Machine Learning (ML) (learning in higher organisms, machine learning; logic, trees and decision making, search algorithms and games, propositional logic,
  algorithms based on nature analogs, statistical learning).
• Supervised Learning (prediction problem, classification, logistic regression, maximum likelihood estimation, Support Vector Machines (SVM), decision trees).
• Optimization Tools in AI and ML (optimization, iterative search algorithms, stochastic maximum descent algorithms and adaptive learning, entropy, KL-divergence).
• Artificial Neural Networks (ANN) and Deep Learning (Multilayer Neural Networks, Backpropagation algorithm, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long and Short
  Term Memory Neural Networks (LSTM)).
• Reinforcement Learning (search in the space of optimal policies, search in the space of prices).
• Unsupervised Learning (creating models from data and recognizing patterns, Data Clustering, Hierarchical Clustering, Clustering with k-means; Probabilistic models: Gaussian kernels and mixtures, parameter estimation via the EM algorithm, Bayesian networks).
• Applications of prediction, classification, grouping/clustering, decision-making in mechanical engineering problems.

Books
− S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach, 3rd Edition. Prentice-Hall, Englewood Cliffs, NJ, 2009.
− K.P. Murphy. Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
− M. Mohri, A. Rostamizadeh, and A. Talwalkar. Foundations of Machine Learning, MIT Press, 2012.
− R.S. Sutton, A.G. Barto. Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 2018.
− I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning, MIT Press, 2016.
− C.M. Bishop. Pattern Recognition and Machine Learning, Springer 2007.
− T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning, Springer, 2011.
− S. Haykin. Neural Networks and Learning Machines, Pearson, 2008.
− W. McKinney. Python for Data Analysis, 2nd Edition. O’Reilly Media, 2017.

Journals
− Artificial intelligence
− IEEE Transactions on Neural Networks and Learning Systems
− IEEE Transactions on Pattern Analysis and Machine Intelligence
− IEEE Transactions on Evolutionary Computation
− IEEE Transactions on Fuzzy Systems
− IEEE Computational Intelligence Magazine
− Journal of Machine Learning Research
− Pattern Recognition
− Neural Networks
− Information Sciences
− Machine Learning
− Annals of Statistics
− Journal of the Royal Statistical Society. Series B: Statistical Methodology

Greek or English

Lectures