MSIT Specialization in Machine Learning and Human-Computer Interaction

Empowering Minds Towards Technological Excellence
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:

  1. Appreciate the importance of visualization in data analytics solutions.
  2. Apply structured thinking to unstructured problems.
  3. Understand a very broad collection of machine learning algorithms and problems.
  4. Learn algorithmic topics of machine learning and mathematically deep enough to introduce the required theory.
  5. Research and develop interactive collaborative systems by applying social computing theories and frameworks.
  6. Design novel ubiquitous computing systems by researching and applying relevant HCI and informatics theories and frameworks.
  7. Design effective, usable, and human-centered interactive systems using prototypes and proof of concepts.
  8. 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.
  9. 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.
  10. 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
Machine Learning and Human-Computer Interaction Specialization MSIT

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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