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Math for ML – 2 Day Virtual Workshop

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Prev Constrained optimisation

Next General non linear least squares

Course Content

Total learning: 66 lessons Time: 10 weeks

Mathematics for ML 0/5
- Lecture1.1
  
  Motivation for Linear Algebra
- Lecture1.2
  
  Motivation for Probability
- Lecture1.3
  
  Motivation for Statistics
- Lecture1.4
  
  Motivation for Calculus
- Lecture1.5
  
  Application of Mathematics and real world case studies
Linear Algebra - Vectors 0/5
- Lecture2.1
  
  Introduction to Vectors
- Lecture2.2
  
  Operations with Vectors and their meaning
- Lecture2.3
  
  Cosine and dor product, inner product of vectors
- Lecture2.4
  
  Basis, vector space and linear independence
- Lecture2.5
  
  Applications of changing basis vectors
Linear Algebra - Matrices 0/7
- Lecture3.1
  
  How matrices transform space
- Lecture3.2
  
  Composition or combination of matrix transformations
- Lecture3.3
  
  Gaussian elimination – solving apples and bananas problem
- Lecture3.4
  
  From Gaussian elimination to inverse matrix
- Lecture3.5
  
  Solving equations using inverse matrix
- Lecture3.6
  
  Determinants of matrices
- Lecture3.7
  
  Identify special matrices
Eigenvalues and Eigenvectors 0/7
- Lecture4.1
  
  Introduction to eigenvalues and eigenvectors
- Lecture4.2
  
  Special eigen-cases
- Lecture4.3
  
  Calculating eigenvectors
- Lecture4.4
  
  Changing to the eigenbasis
- Lecture4.5
  
  Eigenbasis example
- Lecture4.6
  
  Diagonalisation and applications Practice
- Lecture4.7
  
  Introduction to PageRank
Multivariate Calculus 0/1
- Lecture5.1
  
  Intoduction to multivariate calculus, functions and derivatives – visualizing through graphs
Multivariate Calculus 0/8
- Lecture6.1
  
  Definition of a derivative
- Lecture6.2
  
  Differentiation examples & special cases
- Lecture6.3
  
  Product rule
- Lecture6.4
  
  Chain rule
- Lecture6.5
  
  Variables, constants & context
- Lecture6.6
  
  Differentiate with respect to anything
- Lecture6.7
  
  The Jacobian
- Lecture6.8
  
  The Hessian
Intro to optimisation 0/5
- Lecture7.1
  
  Newton-Raphson in one dimension
- Lecture7.2
  
  Gradient Descent
- Lecture7.3
  
  Gradient descent in a sandpit
- Lecture7.4
  
  Steepest strategies
- Lecture7.5
  
  Constrained optimisation
Regression 0/4
- Lecture8.1
  
  Simple linear regression
- Lecture8.2
  
  General non linear least squares
- Lecture8.3
  
  Least squares regression analysis in practice
- Lecture8.4
  
  Fitting the distribution of heights data
Statistics of Datasets 0/5
- Lecture9.1
  
  Mean of a dataset
- Lecture9.2
  
  Variance of one-dimensional datasets
- Lecture9.3
  
  Variance of higher-dimensional datasets
- Lecture9.4
  
  Effect on the mean
- Lecture9.5
  
  Effect on the (co)variance
Principal Component Analysis 0/6
- Lecture10.1
  
  Problem setting and PCA objective
- Lecture10.2
  
  Finding the coordinates of the projected data
- Lecture10.3
  
  Reformulation of the objective
- Lecture10.4
  
  Finding the basis vectors that span the principal subspace
- Lecture10.5
  
  Steps of PCA
- Lecture10.6
  
  PCA in high dimensions
Introduction to Probability 0/8
- Lecture11.1
  
  Disjoint Events + General Addition Rule
- Lecture11.2
  
  Independence
- Lecture11.3
  
  Probability Examples
- Lecture11.4
  
  Disjoint vs. Independent
- Lecture11.5
  
  Conditional Probability
- Lecture11.6
  
  Probability Trees
- Lecture11.7
  
  Bayesian Inference
- Lecture11.8
  
  Examples of Bayesian Inference
Probability Distributions 0/5
- Lecture12.1
  
  Normal Distribution
- Lecture12.2
  
  Binomial Distribution
- Lecture12.3
  
  Normal Approximation to Binomial
- Lecture12.4
  
  Working with the Binomial Distribution
- Lecture12.5
  
  Poison Distribution