Course presentation
In video
In summary
Brief Syllabus
- Intro to probability and statistics
- distributions, information theory, regression and correlation, classification
- Studies and theories in neuro-science
- memory, consciousness
- Classic AI
- logics, knowledge representations, programming paradigms, cognitive architectures, state spaces, causality, Neuro-Symbolic AI, …
- Deep Learning
- generative models, neural network types, interpretability, …
- Different Learning Approaches
- reinforcement, unsupervised, evolutional, Bayesian, meta-learning, multi-task, …
- Natural Language Processing
- text representations, Transformer, computational and language models, semantics
Sources of the course
- Books
- Copeland, Jack - Artificial Intelligence: A Philosophical Introduction (1993)
- Jean-Claude Bringuier - Conversations with Jean Piaget (1977)
- Edward de Bono - Parallel/Lateral Thinking (1995)
- French, C. C., & Colman, A. M. - Cognitive psychology (1995)
- Elizabeth F. Loftus - Visual Perception: An Introduction (2012)
- Jeff Hawkins
- On Intelligence (2004)
- A Thousand Brains: A New Theory of Intelligence (2021)
- Elaine Rich, Kevin Knight - Artificial Intelligence (1991)
- Online courses
- edX, Coursera, udacity, campus.gov.il
- DeepLearning.ai, Microsoft, kdnuggets
- University courses
- Standford
- MIT
- Technion
- Bar-Ilan
- More
- Caltech, Virginia Tech, Duke, Sherbrooke, NPTEL
In syllabus PDF file
Detailed syllabus of the course or here: