Courses with Gaps

Computer devices, Data collection, Evidence Collection, Extraction and preservation of evidence, Data Recovery, Evidence preservation, verification & authentication, Data Discovery & Identification, Data Analysis, Computer Forensics Tools, Data Hiding Techniques, Computer forensics and mobile forensics.

This is an introductory course on Artificial Intelligence. The topics include; Overview: foundations, scope, problems, and approaches of AI. Intelligent agents: reactive, deliberative, goal-driven, utility-driven, and learning agents; Artificial Intelligence programming techniques. Problem-solving through Search: forward and backward, state-space, blind, heuristic, problem-reduction, A, A*, AO*, minimax, constraint propagation, neural, stochastic, and evolutionary search algorithms, sample applications. Knowledge Representation and Reasoning: ontologies, foundations of knowledge representation and reasoning, representing and reasoning about objects, relations, events, actions, time, and space; predicate logic, situation calculus, Content logics, reasoning with defaults, reasoning about knowledge, sample applications. Planning: planning as search, partial order planning, construction and use of planning graphs. Representing and Reasoning with Uncertain Knowledge: probability, connection to logic, independence, Bayes rule, bayesian networks, probabilistic inference, sample applications. Decision-Making: basics of utility theory, decision theory, sequential decision problems, elementary game theory, sample applications. Machine Learning and Knowledge Acquisition: learning from memorisation, examples, explanation, and exploration. learning nearest neighbor, naive Bayes, and decision tree classifiers, Q-learning for learning action policies, applications. Sample Applications of AI, student project presentations.

Introducing the history and concepts of epidemiology, using principles and methods of both descriptive and analytical epidemiology to address public health problems and issues. Practical application of epidemiology, measures of morbidity and mortality, descriptive epidemiology, causation, source of epidemiological data, epidemiological studies’ design, measure of effect, data interpretations issues, and screening for disease in the community.