COMPUTER SCIENCE

Learning Outcomes

Outcomes

On completion of this course both undergraduate and graduate students will be able to:

  • identify the characteristics of datasets and compare the trivial data and big data for various applications (SLO1).
  • understand machine learning techniques and computing environment that are suitable for the applications under consideration (SLO2).
  • solve problems associated with batch learning and online learning, and the big data characteristics such as high dimensionality, dynamically growing data and in particular scalability issues (SLO3).
  • develop scaling up machine learning techniques and associated computing techniques and technologies for various applications (SLO4).
  • implement various ways of selecting suitable model parameters for different machine learning techniques (SLO5).
  • integrate machine learning libraries, and mathematical and statistical tools with modern technologies like Hadoop distributed file system and MapReduce programming model (SLO6).

Additionally graduate students will be able to:

  • identify current real world problems that can benefit from emerging machine learning techniques and the modern big data technologies (SL07).
  • design machine learning and associated algorithms that can address one of the real world problems that they selected for the experiment (SL08).
  • develop advanced programming framework that can process advanced approaches for the real world problem that they used for the experiment (SL09).
  • implement solutions using the machine learning techniques and the programming framework to obtain acceptable decisions for the real world problems that they used for the experiment (SLO10).

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