Episoder
S1 E1 - Introduction Lecture (1 of 72 Lectures)
2. mai 20183minAn introduction to the course is provided. Practice problems available at onlinemathtraining.Tilgjengelig for kjøpS1 E2 - Section 1: Linear Regression (First Lecture: Linear Regression)
2. mai 20188minStudents will learn about the notion of residual sum of squares. Practice problems available at onlinemathtraining.Tilgjengelig for kjøpS1 E3 - The Least Squares Method
2. mai 201811minStudents will learn how to apply the least squares method to solve the least squares problem.Tilgjengelig for kjøpS1 E4 - Linear Algebra Solution to Least Squares Problem
2. mai 201813minStudents will learn about a linear algebra approach to solving the least squares problem.Tilgjengelig for kjøpS1 E5 - Example: Linear Regression
2. mai 20184minAn example of applying the least squares method is provided.Tilgjengelig for kjøpS1 E6 - Summary: Linear Regression
2. mai 201834sekA summary of linear regression is provided.Tilgjengelig for kjøpS1 E7 - Section 2: Linear Discriminant Analysis (First Lecture: Classification)
2. mai 20181minStudents will be introduced to classification problems. Practice problems available at onlinemathtraining.Tilgjengelig for kjøpS1 E8 - Linear Discriminant Analysis
2. mai 201844sekThe method of linear discriminant analysis is introduced.Tilgjengelig for kjøpS1 E9 - The Posterior Probability Functions
2. mai 20184minIn this lecture, we build a formula for the posterior probability.Tilgjengelig for kjøpS1 E10 - Modelling the Posterior Probability Functions
2. mai 20187minIn this lecture, we model the posterior probability functions.Tilgjengelig for kjøpS1 E11 - Linear Discriminant Functions
2. mai 20186minStudents will learn what linear discriminant functions are.Tilgjengelig for kjøpS1 E12 - Estimating the Linear Discriminant Functions
2. mai 20186minIn this lecture, we estimate the linear discriminant functions.Tilgjengelig for kjøpS1 E13 - Classifying Data Points Using Linear Discriminant Functions
2. mai 20183minStudents will learn how to classify data points using linear discriminant functions.Tilgjengelig for kjøpS1 E14 - LDA Example 1
2. mai 201814minStudents will see an example of applying linear discriminant analysis.Tilgjengelig for kjøpS1 E15 - LDA Example 2
2. mai 201818minAnother example of applying linear discriminant analysis is provided.Tilgjengelig for kjøpS1 E16 - Summary: Linear Discriminant Analysis
2. mai 20182minA summary of linear discriminant analysis is provided.Tilgjengelig for kjøpS1 E17 - Section 3: Logistic Regression (First Lecture: Logistic Regression)
2. mai 20181minThe method of logistic regression is introduced. Practice problems available at onlinemathtraining.Tilgjengelig for kjøpS1 E18 - Logistic Regression Model of the Posterior Probability Function
2. mai 20183minIn this lecture, we model the posterior probability function.Tilgjengelig for kjøpS1 E19 - Estimating the Posterior Probability Function
2. mai 20189minIn this lecture, we introduce a strategy for estimating the posterior probability function.Tilgjengelig for kjøpS1 E20 - The Multivariate Newton-Raphson Method
2. mai 20189minStudents will learn how the Multivariate Newton-Raphson method is used to maximize a function.Tilgjengelig for kjøpS1 E21 - Maximizing the Log-Likelihood Function
2. mai 201814minIn this lecture, we apply the multivariate Newton-Raphson method to the log-likelihood function and learn about iterative reweighted least squares.Tilgjengelig for kjøpS1 E22 - Example: Logistic Regression
2. mai 201810minStudents will learn how to apply logistic regression to solve a classification problem.Tilgjengelig for kjøpS1 E23 - Summary: Logistic Regression
2. mai 20181minA summary of logistic regression is provided.Tilgjengelig for kjøpS1 E24 - Section 4: Artificial Neural Networks (First Lecture: Artificial Neural Networks)
2. mai 201836sekAn introduction to artificial neural networks is provided. Practice problems available at onlinemathtraining.Tilgjengelig for kjøpS1 E25 - Neural Network Model of the Output Functions
2. mai 201813minIn this lecture, we build a neural network model for the output functions using a neural network diagram.Tilgjengelig for kjøp
