The course is designed to study current advancements in data collection and analysis techniques for machine learning and vision algorithms
The course will aim to teach students the importance of performance characterization of algorithms with main focus on vision algorithms (such as edge detection, corner detection, blob detection, descriptors, image matching algorithms and application specific algorithms for example, panorama stitching, tracking, navigation etc). It will cover conventional methods such as ROC curves, Precision-Recall curve, sensitivity-specificity graphs, F-measure and Accuracy. The main focus will be to understand the application of above mentioned performance metric s and their limitations. The course will also discuss statistical methods commonly used in other domains such as Null hypothesis testing using parametric methods and non-parametric methods. Parametric methods include chi-square test, T-tsets etc. Whereas, non-parametric methods include ANOVA, McNemar’s test, signed tests etc. Furthermore, the importance of using sufficiently large amount of data will be discussed which will also include the use of synthetic and real data.
Course Learning Outcomes
Upon completion of this course, students will:
• Select appropriate analytical tools for performance characterization of algorithms
• Identify the appropriate amount of data for performance comparisons
• Be able to use tools for different statistical tests such as Excel, SPSS, WEKA etc.
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