• Visualization For Analytics

    Introduces multivariate regression and random forests for modeling data. Addresses data access, variable selection and model diagnostics. Introduces foundations for visual thinking. Reviews common statistical graphics such as dot plots, box plots, q-q plots. Addresses more advanced methods such as scatterplot matrices enhanced by smoothed or density contours, and search tools for finding graphics with…

  • Visualization For Analytics

    Introduces multivariate regression and random forests for modeling data. Addresses data access, variable selection and model diagnostics. Introduces foundations for visual thinking. Reviews common statistical graphics such as dot plots, box plots, q-q plots. Addresses more advanced methods such as scatterplot matrices enhanced by smoothed or density contours, and search tools for finding graphics with…

  • Interpretable Machine Learning

    One of the most common tasks performed by data scientists and data analysts is prediction and machine learning. Machine learning combines advanced topics in statistics, probabilities, linear algebra, and calculus to design mathematical models that learn from data or experience to solve new problems. Computers usually do not explain their predictions which is a barrier…

  • Visualization For Analytics

    Introduces multivariate regression and random forests for modeling data. Addresses data access, variable selection and model diagnostics. Introduces foundations for visual thinking. Reviews common statistical graphics such as dot plots, box plots, q-q plots. Addresses more advanced methods such as scatterplot matrices enhanced by smoothed or density contours, and search tools for finding graphics with…

  • Interpretable Machine Learning

    One of the most common tasks performed by data scientists and data analysts is prediction and machine learning. Machine learning combines advanced topics in statistics, probabilities, linear algebra, and calculus to design mathematical models that learn from data or experience to solve new problems. Computers usually do not explain their predictions which is a barrier…

  • Interpretable Machine Learning

    One of the most common tasks performed by data scientists and data analysts is prediction and machine learning. Machine learning combines advanced topics in statistics, probabilities, linear algebra, and calculus to design mathematical models that learn from data or experience to solve new problems. Computers usually do not explain their predictions which is a barrier…

  • Applied Deep Learning

    Using machine learning in real-world problems often requires more skills than those needed to apply machine learning in academic problems. It is common, for instance, that the number of labeled samples is small but abundance of unlabeled samples is available or the number of labeled samples from different classes are extremely imbalanced. It is sometimes…

  • Applied Deep Learning

    Using machine learning in real-world problems often requires more skills than those needed to apply machine learning in academic problems. It is common, for instance, that the number of labeled samples is small but abundance of unlabeled samples is available or the number of labeled samples from different classes are extremely imbalanced. It is sometimes…

  • Advanced Gpu Programming And Deep Learning

    This course expands on the GPU architecture and programming concepts introduced in ECE 555 by detailing advanced architectural components, such as Tensor Processing Units (TPUs), and their role in accelerating Deep Learning (DL) applications. Lectures study example DL applications, such as object recognition, and how the GPU instruction sets, e.g., Parallel Thread Execution (PTX), are improved to accelerate…

  • Visualization For Analytics

    Introduces multivariate regression and random forests for modeling data. Addresses data access, variable selection and model diagnostics. Introduces foundations for visual thinking. Reviews common statistical graphics such as dot plots, box plots, q-q plots. Addresses more advanced methods such as scatterplot matrices enhanced by smoothed or density contours, and search tools for finding graphics with…