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