Course Content
-
Mathematics for ML 0/5
-
Motivation for Linear AlgebraLecture1.1
-
Motivation for ProbabilityLecture1.2
-
Motivation for StatisticsLecture1.3
-
Motivation for CalculusLecture1.4
-
Application of Mathematics and real world case studiesLecture1.5
-
-
Linear Algebra - Vectors 0/5
-
Introduction to VectorsLecture2.1
-
Operations with Vectors and their meaningLecture2.2
-
Cosine and dor product, inner product of vectorsLecture2.3
-
Basis, vector space and linear independenceLecture2.4
-
Applications of changing basis vectorsLecture2.5
-
-
Linear Algebra - Matrices 0/7
-
How matrices transform spaceLecture3.1
-
Composition or combination of matrix transformationsLecture3.2
-
Gaussian elimination – solving apples and bananas problemLecture3.3
-
From Gaussian elimination to inverse matrixLecture3.4
-
Solving equations using inverse matrixLecture3.5
-
Determinants of matricesLecture3.6
-
Identify special matricesLecture3.7
-
-
Eigenvalues and Eigenvectors 0/7
-
Introduction to eigenvalues and eigenvectorsLecture4.1
-
Special eigen-casesLecture4.2
-
Calculating eigenvectorsLecture4.3
-
Changing to the eigenbasisLecture4.4
-
Eigenbasis exampleLecture4.5
-
Diagonalisation and applications PracticeLecture4.6
-
Introduction to PageRankLecture4.7
-
-
Multivariate Calculus 0/1
-
Multivariate Calculus 0/8
-
Definition of a derivativeLecture6.1
-
Differentiation examples & special casesLecture6.2
-
Product ruleLecture6.3
-
Chain ruleLecture6.4
-
Variables, constants & contextLecture6.5
-
Differentiate with respect to anythingLecture6.6
-
The JacobianLecture6.7
-
The HessianLecture6.8
-
-
Intro to optimisation 0/5
-
Newton-Raphson in one dimensionLecture7.1
-
Gradient DescentLecture7.2
-
Gradient descent in a sandpitLecture7.3
-
Steepest strategiesLecture7.4
-
Constrained optimisationLecture7.5
-
-
Regression 0/4
-
Simple linear regressionLecture8.1
-
General non linear least squaresLecture8.2
-
Least squares regression analysis in practiceLecture8.3
-
Fitting the distribution of heights dataLecture8.4
-
-
Statistics of Datasets 0/5
-
Mean of a datasetLecture9.1
-
Variance of one-dimensional datasetsLecture9.2
-
Variance of higher-dimensional datasetsLecture9.3
-
Effect on the meanLecture9.4
-
Effect on the (co)varianceLecture9.5
-
-
Principal Component Analysis 0/6
-
Problem setting and PCA objectiveLecture10.1
-
Finding the coordinates of the projected dataLecture10.2
-
Reformulation of the objectiveLecture10.3
-
Finding the basis vectors that span the principal subspaceLecture10.4
-
Steps of PCALecture10.5
-
PCA in high dimensionsLecture10.6
-
-
Introduction to Probability 0/8
-
Disjoint Events + General Addition RuleLecture11.1
-
IndependenceLecture11.2
-
Probability ExamplesLecture11.3
-
Disjoint vs. IndependentLecture11.4
-
Conditional ProbabilityLecture11.5
-
Probability TreesLecture11.6
-
Bayesian InferenceLecture11.7
-
Examples of Bayesian InferenceLecture11.8
-
-
Probability Distributions 0/5
-
Normal DistributionLecture12.1
-
Binomial DistributionLecture12.2
-
Normal Approximation to BinomialLecture12.3
-
Working with the Binomial DistributionLecture12.4
-
Poison DistributionLecture12.5
-
This content is protected, please login and enroll course to view this content!
Prev
Product rule