Statistical Pattern Recognition
Training Provider: NATIONAL UNIVERSITY OF SINGAPORE
Course Reference: TGS-2020502113
S$870
Original: S$2,900
Save S$2,030
About This Course
• To acquire knowledge of fundamentals of statistical pattern recognition (SPR) for automated classification, identification and characterization of data in IoT applications.
• To acquire knowledge of probabilistic estimation approaches for simple distributions in IoT applications
• To acquire skill and hands on experience in coding basic probabilistic classification algorithms from scratch
What You'll Learn
The main objectives of this graduate module are to equip students with the fundamentals of statistical pattern recognition (SPR) algorithms and techniques. PR deals with automated classification, identification, and/or characterisations of signals/data from various sources. Because real world data is noisy and uncertain, we will focus on SPR techniques, with particular emphasis on the theoretical foundations of various techniques. Topics covered include: fundamentals of parameter estimation (maximum likelihood, maximum-a-posteriori, posterior predictive), supervised learning, generative models, naïve Bayes, discriminative models, logistic regression, nonparametric techniques, Bayesian decision theory.
Entry Requirements
Relevant Bachelor's Degree in electrical/electronics/communications/computer engineering, computer science, and related disciplines. Knowledge in probability, statistics, linear algebra and basic programming.
Course Details
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