NCI Courses

Defining Artificial Intelligence; Foundations of AI: philosophy, maths, psychology, computing, linguistics, logic, probability theory; Historical evolution of the field.

Weak vs. strong AI; Narrow vs. general vs. super intelligence; Classical vs. statistical AI; Use cases and examples of applications for the different types of AI.

The opportunities and challenges of AI for business; Building safe, ethical, explainable and responsible AI applications; Use cases and examples of AI applications in business.

Precepts, actions, goals, environment; Rational agents; Environments representation; Agent functions and programs; Types of agents: simple reflex agents, reflex agents with state, goal based agents, utility based agents.

Defining search problems; Searching for solutions; Performance; Uninformed search strategies; Informed Search strategies; Heuristic functions.

Local search algorithms and optimisation problems; Adversarial search.

Propositional Logic; First Order Logic; Inference in First Order Logic; Planning.

Ontological Engineering; Categories and objects; Events; Semantic networks.

Quantifying uncertainty; Bayes’ rules and its use; The semantics of Bayesian networks; Conditional distributions and efficient representation of them; Inference in Bayesian networks.

Types of learning (e.g., deductive vs. inductive, supervised vs. unsupervised vs. semi-supervised, reinforcement learning); Learning from examples (e.g., regression, classification); Brief overview of artificial neural networks; Learning probabilistic models.

Formulating a reinforcement learning problem; Classification of reinforcement learning algorithms (e.g., model-free vs. model-based); Markov Decision Processes; Value functions; Reinforcement learning examples.

Recommender systems types and models (e.g., collaborative filtering, content-based filtering, multi-criteria systems, etc.); Recommender systems challenges (e.g., scalability, sparsity, cold start, etc.); AI techniques applied to improve accuracy and overcome challenges with rec sys; Overview of commercial and public cloud AI services for recommendation (e.g., Amazon Personalize, Microsoft Personalizer, Google Cloud Recommendations AI, Coveo Relevance Cloud, Yusp, etc.). Recommender systems examples and case studies including ethical implications (e.g., Netflix Prize data anonymisation and legal case).