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.

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

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: 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.

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

These introductory courses emphasize quantitative measurements, the treatment of measurement errors, and graphical analysis. A variety of experimental techniques should be employed. The experiments include studies of meters, the oscilloscope, mechanical systems, electrical and mechanical resonant systems, light, heat and viscosity, which are covered in PHY 101 and PHY 102. However, emphasis should be placed on the basic physical techniques for observation, measurements, data collection, analysis and deduction.

Non-starch polysaccharides; Natural processes involved in synthesis and degradation of foods; Photosynthesis; respiration; maturation; senescence; Chemical changes that occur in foods during and after processing; Food pigments and other colourants; Food flavor compounds; Effects of maturation and processing on pigments and flavor compounds