Courses with Gaps

Overview of Artificial Intelligence. History of AI. Goals of AI. AI Technique. Types of AI. Branches and applications of AI. Advantages and Disadvantages. Introduction to Intelligent Agents. Agent Performance, Examples of Agents, Agent Faculties, Rationality, Agent Environment. Agent Architectures. Search. General Classes of AI Search Algorithm Problems. Problem Solving by Search. Types of AI Search Techniques and Strategies. Introduction to the types of problems and techniques in AI. Problem-Solving methods. Major structures used in AI programmes. Knowledge Representation. KR and Reasoning Challenges. KR Languages. Knowledge representation techniques such as predicate logic, non-monotonic logic, and probabilistic reasoning. Semantic Network - types of relationships, semantic network inheritance, types and components. Introduction to Frames. Natural Language Processing (NLP). Introduction to natural language understanding and various syntactic and semantic structures. Introduction to Expert Systems - characteristics, components, types, requirements, technology, development. Programming Languages for AI. Introduction to computer image recognition. Lab work: Group practical in (i) Turing test practical - Students can act out their own version of the Turing test (iii) Facial recognition practical to aid in teaching students how machine learning works with students simulating a facial recognition algorithm. Practical applications of NLP in groups – (i) Question Answering focuses on building systems that automatically answer the questions asked by humans in a natural language (ii) Spam detection application for detecting unwanted e-mails getting to a user's inbox (iii) Sentiment analysis/opinion mining should be used on the web to analyse the attitude, behaviour, and emotional state of the sender, implemented through a combination of NLP and statistics (iv) Practical exercise of machine translation used to translate text or speech from one natural language to another natural language such as the Google Translator (v) Developing a model to provide word processor software for the spelling correction (vi) Developing a model for speech recognition for converting spoken words into text (vii) Implementing a Chatbot to provide the staff/student's chat services. OR Group Practical exercise on agents and its environment using simulation of a colony of ants foraging for food; model simulating a message between agents; model simulating the flocking behaviour of birds; model to apply standard search algorithm to the classic search problem of missionaries and cannibals, and how to use communicating agents for searching networks. Some computer AI animation exercises for any branch of AI. Practical exercise on simple robots coupling and programming. Group project of building a lawn robot for trimming grasses, or any simple design and implementation of robotics. 400 Level

Overview of Artificial Intelligence. History of AI. Goals of AI. AI Technique. Types of AI. Branches and applications of AI. Advantages and Disadvantages. Introduction to Intelligent Agents. Agent Performance, Examples of Agents, Agent Faculties, Rationality, Agent Environment. Agent Architectures. Search. General Classes of AI Search Algorithm Problems. Problem Solving by Search. Types of AI Search Techniques and Strategies. Introduction to the types of problems and techniques in AI. Problem-Solving methods. Major structures used in AI programmes. Knowledge Representation. KR and Reasoning Challenges. KR Languages. Knowledge representation techniques such as predicate logic, non-monotonic logic, and probabilistic reasoning. Semantic Network - types of relationships, semantic network inheritance, types and components. Introduction to Frames. Natural Language Processing (NLP). Introduction to natural language understanding and various syntactic and semantic structures. Introduction to Expert Systems - characteristics, components, types, requirements, technology, development. Programming Languages for AI. Introduction to computer image recognition. Lab work: Group practical in (i) Turing test practical - Students can act out their own version of the Turing test (ii) Facial recognition practical to aid in teaching students how machine learning works with students simulating a facial recognition algorithm. Practical applications of NLP in groups – (i) Question Answering focuses on building systems that automatically answer the questions asked by humans in a natural language (ii) Spam detection application for detecting unwanted e-mails getting to a user's inbox (iii) Sentiment analysis/opinion mining should be used on the web to analyse the attitude, behaviour, and emotional state of the sender, implemented through a combination of NLP and statistics (iv) Practical exercise of machine translation used to translate text or speech from one natural language to another natural language such as the Google Translator (v) Developing a model to provide word processor software for the spelling correction (vi) Developing a model for speech recognition for converting spoken words into text (vii) Implementing a Chatbot to provide the staff/student's chat services. OR Group Practical exercise on agents and its environment using simulation of a colony of ants foraging for food; model simulating a message between agents; model simulating the flocking behaviour of birds; model to apply standard search algorithm to the classic search problem of missionaries and cannibals, and how to use communicating agents for searching networks. Some computer AI animation exercises for any branch of AI. Practical exercise on simple robots coupling and programming. Group project of building a lawn robot for trimming grasses, or any simple design and implementation of robotics.

OLS; Gauss-Markov Theorem; MLE; Specification and misspecification test; Predictive and non-predictive tests; Tests of hypothesis for linear model; The likelihood ratio; wald and language multiplier Tests; Multi-collinearity; Specification bias; GLS; Dummy variables and seasonal variations; Inferences based on asymptotic Distribution Theory

Dynamics of microbial populations in air; water and soil; Distribution and survival of aerial and aquatic microorganisms; Biogeochemical cycling of nutrients and chemical elements; Application of microbial systems in water purification; waste management and pollution control; Recent advances in biotransformation and bioremediation

Bioclimatology, agro climatology, climatology and the built environment, climate change and its impact on rural and urban environments, climate change and sustainable development goals, , and climate change and its implications on humans and various human and economic activities. Definition and delimitation of the “Tropics” Rationale for studying the climatology of the tropics. Radiation conditions in the tropics. Temperatures in the tropics. Tropical precipitation. Tropical disturbances: Tropical weather systems. Applied tropical climatology: Tropical agro climatology; tropical bioclimatology, global circulation system and its influence in the tropics., links between the tropical climates and the temperate climates, climate change and climate-premiership.

Development and re-development process. Property management in relation to social, political, economic, physical and location aspects. Property management process – strategy of estate owners and choice of alternative to achieve owner’s objectives. Estate-cycle and problems of obsolescence. Principles of estate management applicable to management of public and private estate, new towns, expanding towns, local authority redevelopment area. Implications for estate management of legislation pertinent to relationship between the landlord and tenant. Marketing and agency: legal positions of principal and agent; methods of sale promotion, selling market segmentation; market research, market planning, total project management. Parties involved in property development. Data and information for property development. Sources of finance and funding arrangements for development. Feasibility Studies – residential/commercial properties. Effect of government policy in development process with emphasis on land policy implications. Management of institutional housing and commercial estates: principles, procedures, and problems. Rule of thumb in real estate practice. Common scheduling tools, including critical path method and bar charts as an aid in project planning, budgeting, and cost control. Also introduces the gathering, processing, and evaluation of project information for effective project control.

Introduction to basic programming techniques of artificial intelligence (AI); Domain analysis; representation of Knowledge and strategies; control on inference and search; development of interactive intelligence CAI programs; the role of analogical reasoning; The main contents are symbol manipulations and AI problem solving techniques; Topics include LISP primitives; LISP objects and evaluation; recursion and iteration and data abstraction (association lists; properties and DESTRUCT); macros; object-centred programming; symbolic pattern matching and basic solving methods

Introduction to biometrics and digital image processing. Matlab in biometric image/signal processing. Biometric algorithms and systems with emphasis on face, fingerprint, eyes (iris), speech (voice). Automated biometric identification multimodal biometrics. Biometric data: raw data, template data, and data methods. Biometric matching basics: biometric authentication, enrolment, correct user, and incorrect user. Match threshold and matching performance. Setting a threshold. Biometric authentication: matching data, ground truth, calculating errors rates and graphs. Biometric data: Storage of biometric data elements, transactions, errors and quality upgrades. Data security and integrity. Privacy issues and other aspects of biometrics. Applications of biometrics and future trends. Challenging issues: security strength and recognition rates. Alternatives of passwords and smart cards. Lab work: Practical exercise on biometric capture, image processing, matching threshold and performance. Learn the practical aspect of automated biometric identification of multimodal, authentication and calculation of error rates. Work on biometric algorithms, privacy and security of stored biometric data.

Introduction to computers and computing; computer organisation – data processing, memory, registers and addressing schemes; Boolean algebra; floating-point arithmetic; representation of non-numeric information; problem-solving and algorithm development; coding (solution design using flowcharts and pseudo codes). Data models and data structures; computer software and operating system; computer operators and operators precedence; components of computer programs; introduction to object oriented, structured and visual programming; use of MATLAB in engineering applications. ICT fundamentals, Internet of Things (IoT). Elements of software engineering.

Introduction: Medicines and discovery processes. Initial stages: Target discovery; applications of molecular biology, in silico methods and assays; lead discovery; lead optimization; biomarkers. Measurement of drug properties: assay techniques; agonists (full, partial, inverse and biased); competitive antagonists. In vivo models; pharmacokinetic profiles of compounds; pharmacogenetics and formulations. Pre-clinical safety assessment including acute toxicity evaluation, subacute and chronic toxicity studies, reproductive toxicity, carcinogenicity studies, mutagenicity and mechanistic toxicity studies. Limitations of preclinical studies. Problems associated with drug discovery and development. Later stages: Pharmaceutical development; phases of clinical evaluation; ethics (informed consent, good clinical practice, participation criteria, sponsorship) and concepts in clinical trial design. Pharmacovigilance. e-Learning Activity Links are provided to additional resources (further reading, videos, on-line activities) to support the lecture content. Online self-assessment modules on the course content can be provided for formative feedback. Practical: Experiments are designed to reflect the topics covered including spectrophotometry, chromatography, flow cytometry.