Copy of deck

Copy of CS6015: Lecture 21

Lecture 21: Principal Component Analysis (the math)

CS6910: Lecture 6

Sigmoid Neuron to Feedforward Neural Networks

CS6910: Lecture 4

Sigmoid Neuron to Feedforward Neural Networks

CS6910: Lecture 3

Sigmoid Neuron to Feedforward Neural Networks

CS6910: Lecture 2

A (brief/partial) History of Deep Learning

CS6910 Feedback

ASP Interview - IIT Madras

CS6910: Lecture 1

A (brief/partial) History of Deep Learning

CS6910: Lecture 0

Logistics, Syllabus

CS6015: Lecture 42

Lecture 42: Information Theory, Entropy, Cross Entropy, KL Divergence

CS6015: Lecture 41

Lecture 41: Central Limit Theorem

CS6015: Lecture 40

Lecture 40: Markov inequality, Chebychev inequality, Weak law of large numbers

CS6015: Lecture 39

Lecture 39: Moments and moment generating functions: What are they and why do we care?

CS6015: Lecture 38

Lecture 38: Multiple continuous random variables, Bayes' theorem for continuous random variables

CS6015: Lecture 37

Lecture 37: The exponential family of distributions

CS6015: Lecture 36

Lecture 36: Uniform distribution, normal distribution

CS6015: Lecture 35

Lecture 35: Continuous random variables, probability mass function v/s probability density function, cumulative distribution function

CS6015: Lecture 34

Lecture 34: Joint distribution, conditional distribution and marginal distribution of multiple random variables

CS6015: Lecture 33

Lecture 33: Expectation, Variance and their properties, Computing expectation and variance of some known distributions

CS6015: Lecture 32

Lecture 32: Geometric distribution, Negative Binomial distribution, Hypergeometric distribution, Poisson distribution, Uniform distribution

CS6015: Lecture 31

Lecture 31: Describing distributions compactly, Bernoulli distribution, Binomial distribution

CS6015: Lecture 30

Lecture 30:Random Variables, Types of Random Variables (discrete and continuous), Probability Mass Function (PMF), Properties of PMF

CS6015: Lecture 29

Lecture 29: Conditional probabilities, multiplication rule, total probability theorem, Bayes' theorem, independent events

CS6015: Lecture 28

Lecture 28: Probability Space, Axioms of Probability, Designing Probability Functions

CS6015: Lecture 27

Lecture 27: Sets, Experiments, Outcomes and Events

CS6015: Lecture 26

Lecture 26: More fun with counting

CS6015: Lecture 25

Lecture 25: Counting collections

CS6015: Lecture 24

Lecture 24: Counting sequences, Subtraction principle

CS6015: Lecture 23

Lecture 23: Counting Principles - Very Simple Counting, Multiplication Principle

CS6015: Lecture 22

Lecture 22: Singular Value Decomposition (SVD)

CS6015: Lecture 21

Lecture 21: Principal Component Analysis (the math)

CS6015: Lecture 20

Lecture 20: Principal Component Analysis (the wishlist)

CS6015: Lecture 19

Lecture 19: Algebraic and Geometric Multiplicity, Schur's theorem, Spectral theorem for Symmetric matrices, Trace of a matrix

CS6015: Lecture 18

Lecture 18: Diagonalisation (eigenvalue decomposition) of a matrix, computing powers of A

CS6015: Lecture 17

Lecture 17: Change of basis

CS6015: Lecture 16

Lecture 16: The Eigenstory begins, computing eigenvalues and eigenvectors

CS6015: Lecture 15

Lecture 15: Formula for determinant, co-factors, Finding the inverse of A, Cramer's rule for solving Ax=b, Determinant=Volume

CS6015: Lecture 14

Lecture 14: Properties of determinants

CS6015: Lecture 13

Lecture 13: Orthonormal vectors, orthonormal basis, Gram-Schmidt orthogonalization

CS6015: Lecture 12

Lecture 12: Projecting a vector onto another vector, Projecting a vector on to a subspace, Solving Ax=b when no solution exists

CS6015: Lecture 11

Lecture 11: A tiny bit of ML, vector norms, orthogonal vectors, orthogonal subspaces

CS6015: Lecture 10

Lecture 10: The four fundamental subspaces

CS6015: Lecture 9

Lecture 9: Solving Ax=b, Rank Nullity Theorem, Some unsolved mysteries

CS6015: Lecture 8

Lecture 8: Solving Ax=0

CS6015: Lecture 7

Lecture 7: column space of a matrix, null space of a matrix

CS6015: Lecture 6

Lecture 6: Vector spaces, subspaces, independence, span, basis, dimensions

CS6015: Lecture 5

PA = LU, cost of Gaussian Elimination, the practical utility of LU factorisation, computing inverse using Gauss Jordan method

CS6015: Lecture 4

Gauss Elimination, LU factorisation

CS6015: Lecture 3

Lines, planes, solving a system of linear equations (intuition), number of solutions to a system of linear equations