This module aims to give the students a deep understanding of modern macroeconomics theory and models. Topics include Growth Models, Overlapping Generation Models, Employment Models, Real Business Cycle Models, and Dynamic Stochastic General Equilibrium Models.
This module is composed of five units. Each unit will cover a wide range of thought-provoking subject matter in addressing both theoretical and practical issues related machine learning and artificial intelligent
UNIT 1. Introduction to Machine Learning and Artificial Intelligence:
Definition of machine learning (ML) and artificial intelligence (AI)
Historical background and key milestones
Importance and applications of ML and AI in various fields
UNIT 2. Fundamentals of Machine Learning:
Supervised, unsupervised, and reinforcement learning
Training data, validation data, and test data
Feature engineering and feature selection
Evaluation metrics for ML models
UNIT 3. Regression and Classification:
Linear regression
Logistic regression
Decision trees
Random forests
Nearest neighbourhood
Unit 4. Clustering and Dimensionality Reduction:
Hierarchical clustering
Principal Component Analysis (PCA)
UNIT5. Neural Networks and Deep Learning:
Introduction to artificial neural networks (ANN)
Feedforward neural networks
Backpropagation algorithm
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Generative Adversarial Networks (GAN)
This module is composed of five units. Each unit will cover a wide range of thought-provoking subject matter in addressing both theoretical and practical issues related machine learning and artificial intelligent
UNIT 1. Introduction to Machine Learning and Artificial Intelligence:
Definition of machine learning (ML) and artificial intelligence (AI)
Historical background and key milestones
Importance and applications of ML and AI in various fields
UNIT 2. Fundamentals of Machine Learning:
Supervised, unsupervised, and reinforcement learning
Training data, validation data, and test data
Feature engineering and feature selection
Evaluation metrics for ML models
UNIT 3. Regression and Classification:
Linear regression
Logistic regression
Decision trees
Random forests
Nearest neighbourhood
Unit 4. Clustering and Dimensionality Reduction:
Hierarchical clustering
Principal Component Analysis (PCA)
UNIT5. Neural Networks and Deep Learning:
Introduction to artificial neural networks (ANN)
Feedforward neural networks
Backpropagation algorithm
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Generative Adversarial Networks (GAN)
This module is an intermediate level to the theory and practice of macroeconomics. The student should gain a sufficient understanding of macroeconomic theory to understand empirical research and, therefore, give valuable insight into topical issues. Particular reference is made to the relative importance of long-run term growth and macroeconomic policies in the open economy.
The module on Operations Research introduces students to mathematical models and techniques used to derive values of variables for a complex organizational system that optimizes the performance of that system. More specifically, it creates the students’ awareness of linear linear programming, transportation and assignment, network, and queuing models. The applications include industrial processes, management systems, road and transport networks, and telecommunication systems. The course content is based on real-world examples and cases to encourage students to develop their attitude and ability to discover and innovate.
By the end of the module, students should be able to:
- Interpret results of linear programming (application of linear programming, formulation of linear programming models, simplex method, dual linear programming problem, sensitivity analysis, linear programming with matrix algebra);
- Interpret results of transportation and assignment problems;
- Apply network optimization models (terminology of networks, shortest-path problem, minimum spanning tree problem, maximum flow problem, minimum cost flow problem, and network simplex method);
- Apply dynamic programming techniques (characteristics of dynamic programming problems, deterministic dynamic programming);
- Explain queueing theory;
- Work with Project Management with PERT/CPM
- This module introduces students to mathematical models and techniques used to derive values of variables for a complex organizational system that optimizes the performance of that system.
- More specifically, it creates the students’ awareness of linear, transportation and assignment models, network models, and queuing models.
- The applications include industrial processes, management systems, road and transport networks, and telecommunication systems.
- The module content is based on real-world examples and cases to encourage students to develop their attitude and ability to discover and innovate.
Features of the module
This module is a crosscutting module taught in 3 year as specialisation in department of Marketing and Human resource management for undergraduate.
• It is a pillar of learning in current knowledge based society.
• It is taught in blended learning mode of delivery (Online and face to face).
• It helps to deal with information-rich society.
• It is made of both theoretical and practical parts
Learning outcomes
At the end of this module, you will be able to:
1. Basic and fundamental concepts and theories of results oriented management,
2. Steps involved in results oriented management,
3. Anti-poverty agencies mission for community and strategies to address needs,
4. Specific improvement or results
Module developers
1. Pelly MURUNGI, Module Leader
2. Brigitte GAFARANGA, Module Partner
Introductory Microeconomics is to equip students and stand as foundation in their learning of economics