Courses with Gaps

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.

Introduction to Artificial Intelligence: Intelligent Agents and Applications of Artificial Intelligence. Knowledge Representation and Reasoning: Propositional logic, Theory of first order logic, Inference in First order logic, Forward and Backward chaining, Resolution, Probabilistic reasoning, Utility theory, Hidden Markov Models (HMM), Bayesian Networks. Machine Learning: Supervised and unsupervised learning, Decision trees, Statistical learning models, Learning with complete data – Naive Bayes models, Learning with hidden data – EM algorithm, Reinforcement learning. Pattern Recognition: Introduction, Design principles of pattern recognition system, Statistical Pattern recognition, Parametre estimation methods – Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA), Classification Techniques – Nearest Neighbour (NN) Rule, Bayes Classifier, Support Vector Machine (SVM), K – means clustering.

Introduction - Theory of Chemical Neurotransmission Evidence for Acetylcholine as a Cholinergic neurotransmitter. Detection and Bioassay of acetylcholine. Cholinergic receptors, sites of action of acetylcholine at the Neuromuscular junction. Agonists and antagonists of cholinergic transmission. Parasympathomimetic and parasympatholytic drugs. Structure – Activity Relationships among the cholinergic and anticholinergic agents. Cholinesterases and anti- cholinesterases. Properties and uses of anti-cholinesterases (reversible and irreversible). Brief mention of cholinesterase reactivators (pralidoxime, obidoxime) and their uses. Neuromuscular transmission and drugs which enhance neuromuscular transmission. Neuromuscular Blockade. Smooth muscle relaxants. Ganglion stimulation and blockade and their properties.

Fundamental and derived quantities and units. Equation of motion, concepts of force work energy, power, momentum energy conservation. Fields, Electrostatics, Physical Factors governing capacitance, charging and discharging capacitor and their uses in Radiological Equipment, basic x-ray circuitry and many others. Basic computer Architecture and peripherals. Electromagnetic and Electromagnetic induction, Lenz laws, Mutual and Self-induction. principles and construction of the transformer. Transformer Parameters, uses of mutual and self-inductance in autotransformers and High Tension transformers, AC theory and sources of electric power supply. current electricity, solid state devices, Rectification, principles and uses in Radiology, concept of energy. Atomic structure, Bohr’s atom elements of quantum mechanics applied to atom, Heisenberg’s uncertainty theory, de Broglie wavelength, Schrodinger equation and configuration of atoms. The nuclear structure and models, nuclear instability and radioactivity, Wave and Quantum methods of Energy Transfer and applications in radiology, production of X-rays, Radioactivity and radioactive decay, half-life, counters, units of activity and measurement, K- capture. The atom, isotopes, isobars, isomers, nuclear binding energies, and inverse square law, effects of filtration. Luminescence and their applications. Interaction of radiation (emr and particulate radiation) with matter and their applications in medical imaging and radiotherapy. Attenuation of radiation shielding and filtration.

Structure of woods and its influence on wood properties; gross characteristics of wood; macro and microscopic features of hardwoods. Wood and water interactions. General consideration of physical and mechanical properties of wood. Identifications of Nigeria wood at the macroscopic level.

General introduction to various aspects of surveying, use of simple survey equipment; principles and practice of chain surveying, compass traversing. Introduction to survey draughtsmanship.

General introduction to the various facts of land surveying, determination of vertical distance errors, and corrections in levelling cross-sections and profiles: contouring, enlargement and reduction of maps, years. Introduction to Geographic land information systems, use of basic GPS devices.

Sampling theory and its critique; subjective probability; likelihood principles; Bayes theorem; Bayesian analysis of Normal theory inference problems; the Behrens-Fisher problem; assessment of model assumptions; robustness of inference; analysis of variance; estimation of variance components; empirical Bayes; some aspects of multivariate problems; sequential nature of Bayesian inference; prior and posterior distributions of parameters in binomial; poisson; exponential and normal populations; comparison of two normal distributions; predictive distributions; decision theory; utility; risk aversion; extensive form of analysis; two-action problems; point estimation; best population problems; economics of sampling

Installation: Cloudera VM, Jupyter server. Big data retrieval and relational querying: Postgres databases, NoSQL data, MongoDB, Aerospike, and Pandas for data aggregation and working with data frames. Big Data Integration: Splunk and Datameer. Big Data Processing: Apache Spark, Hadoop, Spark Core (Spark MLlib and GraphX). Big Data Applications (Graph Processing). Big Data Streaming Platforms for Fast Data. Lab Work: Analysing Twitter Data using Spark and MongoDB. Learn Big Data analytics skills. Practical procedure for the crafting of an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from data. Use the practical exercises to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. Hands-on exposure to Hadoop and Spark (or any of the BD tools), build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases. Practical assignment of BD security.

Introduction to microbiology and biochemistry. Classification and growth characteristics of micro-organisms. Enzymes Engineering: including enzyme kinetics, aerobic and anaerobic respirations, metabolic pathways, cell growth kinetics and models