Neural Networks for Pattern Recognition

An Introductory Course in Artificial Intelligence and Data Mining for Scientists, Engineers, Econometrists and other Applied Scientists


About This Course: Neural networks (NNs), or artificial neural networks (ANNs), are a computational model used in machine learning, computer science and other research disciplines. From a mathematical viewpoint, the study of neural networks begun around the 1940s. The goal of the neural network is to solve problems – usually modelling and classification problems – by mimicking humans. Neural networks study is all about doing useful computations. Simply, it is endowing computers the ability (that is, techniques) to reason like we humans do – that is, endowing computers with artificial intelligence. Humans are very good in learning from examples; by modelling relationships from data, neural networks allow computers to extract relationship (i.e. learn) from examples. Note, by techniques, we mean computer codes.


This course is a comprehensive treatment of the fundamentals of neural networks from the perspective of pattern recognition. It will focus on the mathematical details and the applications of neural networks. It will introduce the basic concepts of pattern recognition, the techniques for modelling probability density functions, the properties of the multi-layer perceptron and the radial basis function network models, and more.


Applications of neural networks include autopilot aircrafts in aerospace industry; automobile guidance systems; military weapon orientation and steering; real estate appraisal, loan and mortgage screening, bond rating, portfolio trading computer programs, corporate financial analysis, and currency value prediction; manufacturing process control; cancer cell analysis, prosthetic design, and transplant time optimizer in medical sciences; speech recognition; image and data compression, automated information services, and real-time spoken language translation in telecommunications; face and optical character recognition; natural calamities’ prediction; and many more.


Training Cost: ₦ 315,000.00    

Course Dates: 
Saturday 6 January 2018 – Saturday 10 March 2018


Course Time: Saturdays, 6pm – 9pm, 6 Jan – 10 Mar 2018


Prerequisites: Basic IT skills and basic programming skills in any programming language. Our Scientific Programming Using Python course, Data Science with R course or Scientific Programming with C++ course would be an ideal introduction to this course. Also, a first year undergraduate-level mathematics or background in engineering, physics, mathematics, economics, finance or another applied science with some mathematical content will be helpful.



Information on how to book one or more of our courses can be found here .


Some Applications of Neural Network for Pattern Recognition

   •     Character Recognition





   •     A Self-driving Car





   •     Machine Learning with NN for an Embedded System





   •     The Classification of Complex Geographic Datasets





   •     Neural Network for Accelerometer Data Processing





   •     Deep Learning for Computational Biology





   •     Robot Cognitive Control





   •     Self-driving Car Computer Vision System




Availability Type: 
Key Features
Neural networks explained in a multi-disciplinary context
Extensive graphical illustrations of complex mathematical concepts for quick and easy understanding
In-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting
Covers model development and interpretation of results, data pre-processing, input selection, model development and validation, model uncertainty assessment, and many more.
Introduction to Neural Networks
Single-Layer Networks
The Multi-Layer Perceptron
Radial Basis Function Neural Networks
Learning & Generalisation
Pattern Recognition Applications
Neuro-Fuzzy Systems