This class will involve a project which will serve several purposes. First, it will give you the opportunity to
explore in-depth the multidimensional (pun intended) facets of the HTI course. Second, it will support the development
of your critical thinking and hands-on application skills; in my opinion, this is one of the primary goals of university education.
The students will form groups of two or three (Remember: One is a maverick, two is a pair, three is a team, and four is a crowd)
to undertake a project. The students are expected to work with the instructor in the first three weeks to identify a HTI-related
problem for each group, the hardware and/or software resources that would be needed, and the methodologies that may be required
to work on it.
Problem Statement
What is the problem you are trying to solve and why? What are the potential applications that will come out of the developed
solution? What will be the potential impact of the solution?
Literature Survey
A survey of what other research groups/labs/Industry have done to solve the above problem. What were the developed solutions
that came out of those studies? How are you planning to take these solutions further/fix shortcomings of these solutions/extend
these solutions for your application or increase the performance?
[For Track 1 – Experimentation] Experiment Protocol
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An overview of the experiment protocol you are planning to operationalize going ahead and how it is based on the literature
survey you did. How will this experiment protocol help you with finding solutions to your above problem statement? Other
factors related to the experiment such as how many users, number of trials, control vs. experiment groups, intervention
in the experiment, what apparatus will you use, what kind of data will you collect, how will you take care of privacy
and ethical concerns, etc.
-
What machine learning/signal processing/other scientific methodologies and hardware will you use going ahead and how are you
planning to source them. Please use lots of plots/other visualization techniques and quantifiable numbers information in this
section of your presentation. This will give the instructor an idea of your thinking process and approach.
[For Track 2 – Research] Dataset Info
-
If you did not collect the dataset yourself: what is the nature of the dataset and why did you choose that particular dataset,
how was the data collected by its authors, were there any ethical concerns (if so, how did they address them), how many features
and datapoints are there in that dataset, etc. How will the dataset help in providing solutions to your problem statement?
-
How was the dataset pre-processing done by you? For e.g., were there any missing features, how did you interpolate them
(e.g., regression), did your dataset require you to do features dimensionality reduction using PCA, LDA, etc., did you use
any algorithm to see which features are more important than others or if you will need to collect/procure more data to
get more valuable features. Please use lots of plots/other visualization techniques and quantifiable numbers information
in this section of your presentation. This will give the instructor an idea of the depth of your thinking process and approach.
Timeline and Deliverables
What is the timeline of your project between midterm and endterm (please use a Gantt chart or similar ways of representing this
information)? What are the possible challenges that you foresee in your project (hardware/software, data collection, algorithmic
complexity, etc.) and how do you think you will tackle them? Finally, exactly what will be your deliverables by endterm. Choose
these deliverables carefully as we will come back to them during the endterm presentation.
Note: All the members of a project group must be present during the group presentation which should be jointly delivered. Your
presentation during the midterm must not exceed 8 minutes and we will keep a maximum of 4 minutes for Q&A.
Endterm Project Evaluation Criteria
Problem Statement
What is the problem you are trying to solve and why? What are the potential applications that will come out of the developed
solution? What will be the potential impact of the solution?
Literature Survey
A survey of what other research groups/labs/Industry have done to solve the above problem. What were the developed solutions
that came out of those studies? How are you planning to take these solutions further/fix shortcomings of these solutions/extend
these solutions for your application or increase the performance?
[For Track 1 – Experimentation] Experiment Protocol
-
An overview of the experiment protocol you are planning to operationalize going ahead and how it is based on the literature
survey you did. How will this experiment protocol help you with finding solutions to your above problem statement? Other
factors related to the experiment such as how many users, number of trials, control vs. experiment groups, intervention
in the experiment, what apparatus will you use, what kind of data will you collect, how will you take care of privacy
and ethical concerns, etc.
-
What machine learning/signal processing/other scientific methodologies and hardware will you use going ahead and how are you
planning to source them. Please use lots of plots/other visualization techniques and quantifiable numbers information in this
section of your presentation. This will give the instructor an idea of your thinking process and approach.
[For Track 2 – Research] Dataset Info
-
If you did not collect the dataset yourself: what is the nature of the dataset and why did you choose that particular dataset,
how was the data collected by its authors, were there any ethical concerns (if so, how did they address them), how many features
and datapoints are there in that dataset, etc. How will the dataset help in providing solutions to your problem statement?
-
How was the dataset pre-processing done by you? For e.g., were there any missing features, how did you interpolate them
(e.g., regression), did your dataset require you to do features dimensionality reduction using PCA, LDA, etc., did you use
any algorithm to see which features are more important than others or if you will need to collect/procure more data to
get more valuable features. Please use lots of plots/other visualization techniques and quantifiable numbers information
in this section of your presentation. This will give the instructor an idea of the depth of your thinking process and approach.
Analysis, Performance Metrics and Deployability of the Solution
What were the performance metrics and how much were they achieved with data analysis? How do these performance metrics show that your
solution works? Can the solution be deployed in the real-world to solve the problem you have chosen? If so, how? What may some
challenges be for the deployed solution when it will scale up?
Impact
How will the solution impact the problem outline above? What will be the larger implications of your proposed solution? Basically,
this is a wrap up of why exactly this problem statement was worth working on and what change will this bring about in the world
based on the experimental data collection done by you?
Note: All the members of a project group must be present during the group presentation which should be jointly delivered.
Additionally, your endterm presentation during must not exceed 10 minutes and we will keep a maximum of 5 minutes for Q&A.
Please understand that since this is a group project, every member of the group is expected to know about every aspect of the
project when the jury will ask questions. Please do not say later that for e.g., “I was only in-charge of data analysis and
someone else was in-charge of literature survey.”
