Machine Learning (MSIT)
Program Aim
The Machine learning and Human-computer Interaction specialization curriculum prepares students for careers in machine learning, artificial intelligence, and human computer interaction. The study of machine learning and human computer interaction brings together original research and review articles discussing the latest developments in the field of human-machine interaction based on machine learning and to create a conversation between people and machines that seems natural and intuitive. Human Computer Interaction (HCI) focuses on the design, evaluation, and use of information and communication technologies with an explicit goal to improve user experiences, task performance, and quality of life. HCI is currently being shaped and shaping the applications of artificial intelligence (AI) and intelligent augmentation (IA). This is leading to the rapid emergence of new and exciting research topics. These topics and the questions derived from them are extending and challenging our current theoretical foundations and research methodologies.
Program Objective
The student who graduates with a major in Machine learning and Human-computer Interaction will be able to:
- To understand the basic theory underlying machine learning.
- To be able to formulate machine learning problems corresponding to different applications.
- To understand a range of machine learning algorithms along with their strengths and weaknesses.
- To be able to apply machine learning algorithms to solve problems of moderate complexity.
- To apply the algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models.
- To encourage empirical research (using valid and reliable methodology, with studies of the methods themselves where necessary).
- To promote the use of knowledge and methods from the human sciences in both design and evaluation of computer systems.
- To promote better understanding of the relation between formal design methods and system usability and acceptability.
- To develop guidelines, models, and methods by which designers may be able to provide better human-oriented computer systems.
Program Learning Outcomes
The program learning outcomes (PLOs) set out the academic learning, skills, and achievements that the student must reliably demonstrate before graduation.
Upon successful completion of the courses in the Machine learning and Human-computer Interaction program, the graduating students shall demonstrate:
- Appreciate the importance of visualization in data analytics solutions.
- Apply structured thinking to unstructured problems.
- Understand a very broad collection of machine learning algorithms and problems.
- Learn algorithmic topics of machine learning and mathematically deep enough to introduce the required theory.
- Research and develop interactive collaborative systems by applying social computing theories and frameworks.
- Design novel ubiquitous computing systems by researching and applying relevant HCI and informatics theories and frameworks.
- Design effective, usable, and human-centered interactive systems using prototypes and proof of concepts.
- Critique interaction designs on their usability, human-centeredness, and satisfaction of requirements; evaluate the fitness of requirements, goals, and research methods; make recommendations; and create and defend alternative designs.
- Exhibit sound judgment, ethical behaviour, and professionalism in applying HCI concepts and value-sensitive design to serve stakeholders and society, especially in ethically challenging situations.
- Collaborate in teams fairly, effectively, and creatively, applying group decision-making and negotiation skills.
Program Structure
| Year | Semester | Block | code | Subjects | Credits |
| Core Courses | |||||
| Year 1 | Semester 1 | Block 1 | IT4101 | Advance Computer Networks | 4 |
| Block 2 | IT4103 | Software Project Management | 4 | ||
| Block 3 | IT4102 | Advanced Database Management System | 4 | ||
| Semester 2 | Block 4 | MG4102 | Research Methodology | 4 | |
| Block 5 | IT4105 | Software Testing and Quality Assurance | 4 | ||
| Block 7 | IT4106 | Advanced Java Programming | 4 | ||
| Specialization Courses | |||||
| Year 2 | Semester 3 | Block 7 | ML4201 | Human Computer Interaction | 4 |
| Block 8 | ML4202 | Machine Learning & Human -Computer Interaction | 4 | ||
| Block 9 | ML4202 | Computational Data Analysis | 4 | ||
| Semester 4 | Block 10-12 | IT4205 | Project Work | 9 | |
| Total Credits | 45 | ||||
Mr.Peter Nkhoma
Bachelor of Science in Information Technology
“My experience with Texila American University has been really great, it’s a wonderful experience and the lecturers are so friendly and interactive, they are always concerned about the studies of students. Texila American University has a standard education with very latest updated curriculums with concepts applicable to the current world. Latest technologies and wonderful libraries, it was always a wonderful feeling to be a student and to one day be called product of Texila American University.”
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FAQs
The Machine Learning Specialization MSIT program at TAU Guyana is a master’s degree program designed to provide students with advanced knowledge and skills in machine learning algorithms, data analysis, artificial intelligence, and predictive modeling techniques.
Admission requirements typically include a bachelor’s degree in computer science, engineering, mathematics, or a related field from an accredited institution, academic transcripts, letters of recommendation, a statement of purpose, and sometimes standardized test scores like the GRE. Proficiency in programming languages and data analysis is also beneficial.
The curriculum includes core MSIT courses along with specialized courses focused on machine learning, such as Machine Learning Fundamentals, Data Mining, Deep Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, and Advanced Topics in Machine Learning.
Graduates can pursue various career paths in machine learning, data science, artificial intelligence, predictive modeling, data analysis, research and development, machine learning engineering, data engineering, and data-driven decision-making roles in industries such as IT, finance, healthcare, e-commerce, and technology companies.
Machine learning works by feeding data into algorithms that iteratively learn from patterns and make data-driven predictions or decisions without being explicitly programmed.