CCMAS Course Search

Dynamics of aquatic resources; Theoretical considerations of primary and secondary energy budgets; Factors affecting energy budgets

Nature of arbitration; its origin and application to valuation. Act reference to arbitration by consent. Types of arbitration. Statutory and conventional advantages of arbitration. Arbitration and umpires. Principal rules of arbitrators. Reference by order of court; neutral and tripartite panel. Procedure for arbitration award. Expert evidence and proof of evidence. Arbitration of real estate and related disputes in construction industry. Particular skills required of a professional. Costing by arbitrators in the construction industry. Real estate documents and arbitration clauses. Resolution of disputes by arbitration in the allied professions in the construction industry. Distinction between valuation, arbitration and action methods of enforcing, impeaching and award. Arbitration agreement, composition of arbitral tribunal, jurisdiction of arbitral tribunal, award and termination of proceedings, recourse against award, recognition and enforcement of awards; difference between arbitration and conciliation.

Introduction to graphics description and use of drawing equipment, basic drafting techniques, line drawing, scale and proportions, lettering convention, representation on drawing basic descriptive geometry, surface development, inclined cuts and true sections. Effect of light on shape and form, shadow protection rendering techniques. Simple projections, various types of tracing and drawing papers. Standard sizes, right and wrong tiles. Design process with example of a small house project, importance of various spaces, linking of various spaces. Development of elevations. Difference between presentation drawings and working drawings. Introduction to Draughtsman ship. Site: relation of site with design solution, site selection factors for various types of topographic construction of different scales and their uses in practice.

The course sets out to provide the methods of evaluating art as a configuration of form. These include descriptive and analytical procedures that aid the definition of style/stylistics and meaning making in the art object. The course accommodates foundational knowledge in such theoretical and methodological conventions as formalism, iconography and iconology, semiotics, critical theory, and deconstruction. 400 LEVEL COURSES NEW MEDIA ART

The course introduces majors in art history to the history and methods of the discipline. It surveys the approach and methods adopted by Gorgio Vasari’s Life of the Artists and its artistic biography approach. Johan Joachim Winkelmann, Jacob Burckhardt among others are explored. Topics such as determination of periods, stylistics and atelier attribution and provenance studies, the primitive and the progressive chronology applied to African art history constitute the core of this course.

This course provides the conceptual frame of art in general education, calling attention to the location of art within the overall aim of educating the child. Studies in the artistic abilities of children, typologies of creative achievements concerning age and social class complex, from the age of scribbling to the age of artistic awareness, evaluating child art, strategies for building confidence artistic attainment in the child in critical practice and perception, understanding the workings of the art curricular for child pedagogy.

Semen collection and evaluation; Methods of semen preservation; Species differences and preferences; Semen extenders: advantages and disadvantages; Evaluation of fresh and preserved semen for insemination; Disease control through artificial insemination; Problems associated with artificial insemination

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

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.