Data is at the center of the so-called “fourth paradigm of scientific research” that will spawn new sciences useful to the society. Data is also the new and extremely strong driving force behind many present-day applications, such as smart city, manufacturing informatics, and societal security, to name a few. It is thus imperative that our students know how to handle data, analyze data, use data and draw insights from data. This course aims at acquainting the students with the analytical foundation of data handling techniques.
The course consists of a series of lectures and seminar talks with substantial student participation, in the form of research and presentation in response to posted questions about main topics in data analytics and modeling. The course will consist of a balanced exposition of what, how and why as related to subjects at the core of data analytics and modeling.
1. Scope
Broad topics covered in the course include:
• Probability distribution & parameter estimation
• Regression & curve fitting
• Data modeling: generative models, discriminative models, mixture models, latent variable models & hybrid distributions
• Data modeling for large observations: modeling of sequential data or observations of large dimensionality; hidden Markov models, Markov random fields, & graphical models
• Pattern recognition & decision theory
• Machine learning, neural networks and deep learning
We’ll spend on average about 2 weeks on each topic. The course puts breadth first and depth second as it is offered to students of varying technical backgrounds.
2. Format
For each topic, after an introductory lecture by the lecturer, individual students will be assigned to conduct research, answer generic as well as specific questions and return with presentations to the class. Each student presentation is of duration 20-30 min., followed by ~10 min. questions and discussion. Students who are assigned to address specific topics and questions have one week time to prepare for the presentation. Presented material must first make sense, logical as well as technical, to the presenting student in order to be able to “teach” and “convince” the class how best to learn and understand the subject matter. Generic questions applicable to any engineering topic are:
‒ What are the problems (and/or observations) that gave rise to the particular topic & concept? (The original motivation);
‒ What are the problem formulations with relevant assumptions that have been proposed? (The methodology and formulation);
‒ What are the ensemble of techniques that were developed to solve the problem? (The tools and capabilities);
‒ How do these techniques solve the problem or contribute to the solutions? (The solution mechanism);
‒ What problems beyond the original motivation will the topic and the related techniques be able to solve? (New and novel applications);
‒ What are the limitations of the solutions proposed so far? Any remaining open problems in the topic? (Research opportunities).
Each presentation should attempt to answer these questions to the best of the presenter’s ability and the availability of the relevant material. Other topic-specific questions may also be posted and addressed in student presentations.
After a topic is addressed by student presentations, one or two commentary sessions by the lecturer on the subject will follow so as to complete the systematic development of understanding of the subject/topic.
The course will be primarily conducted in English. To reflect the applicability of the subject matter to local problems, local languages may also be used as the circumstance calls for it.