Micro-Credential in
Neural Network Basics
BAI12043
Course Category:
Digital Skill & Technology, Artificial Intelligence, OOP, Technology Skill
Mode of Delivery:
Conventional / ODL
Language of Instruction:
English
Level of the Course:
Year 1
Prerequisite:
Not Applicable
Credit Value:
3 credits
Duration:
14 Academic Weeks
Total Learning Hours:
120 hours
Synopsis:
This course introduces the basic mathematical concepts for understanding nonlinearity, feedback in neural networks and the fundamental techniques and principles of nueral computation. The course discusses recurrent networks, recurrent feedback loops, statistical pattern recognition, network dynamics and some common models and their applications.
Learning Outcomes
- Explain perceptrons and dynamical theories of recurrent networks
- Apply back propagation and Hebbian learning
- Apply Stochastic Gradient Descent and Reiforcement learning
Assessment Methods
and Types:
60% Assignments & 40% Final Exam
Content Outline:
- Perceptrons: Simple and Multilayer, Perceptrons as Models of Vision, Linear Networks
- Retina, Lateral Inhibition and Feature Selectivity, Objectives and Optimization
- Hybrid Analog-Digital Computation, Ring Network, Constraint Satisfaction, Bidirectional Perception
- Hamiltonian Dynamics, Antisymmetric Networks, Excitatory-Inhibitory Networks, Learning
- Associative Memory, Clustering, Conditioning, Backpropagation
- Stochastic Gradient Descent, Reinforcement Learning
Additional Requirement:
Computer
Entry Requirement:
Minimum Age: 19 years old
Click [here] to contact us if you are below the Minimum Age
Time Table
Coming Soon
Course Price:
RM1,700.00
Why Choose a Micro-Credential Credit Bearing?
Pathway to Academic Qualification
Stackable & Modulised Learning
Credits
Value
Competency & Versatility
Recognition & Accredited
14 Academic Weeks
Join RU Class
Doing While Learning
Digital Credentials
Life Long Learning
Pathway to the Qualification
Entry Requirement of the Origin Programme
