01:00:59

Introduction to Machine Learning Lecture 7: Gradient Descent

01:09:06

Introduction to Machine Learning Lecture 6: Bayesian Decision THeory

01:12:38

Introduction to Machine Learning Lecture 5: k-means clustering and Gaussian Mixture Models

01:18:25

Introduction to Machine Learning Lecture 4: Density estimation

01:15:38

Introduction to Machine Learning Lecture 3: Curve fitting

01:15:32

Introduction to Machine Learning Lecture 2: Datasets and Ethics

01:08:11

Introduction to Machine Learning Lecture 1: Introduction

02:31

Computational Creativity 2023

54:46

Computational Creativity Lecture 22: Generative models for X (vector graphics, layouts, animation)

01:01:08

Computational Creativity Lecture 21: Generative models for 3D

01:09:18

Computational Creativity Lecture 20: 3D representations and neural radiance fields (NeRFs)

54:58

Computational Creativity Lecture 19: Generative Models for Music

59:21

Computational Creativity Lecture 18: Diffusion Developments

58:50

Computational Creativity Lecture 16: CLIP and its applications

01:02:11

Computational Creativity Lecture 17: DALL-E 2 and Stable Diffusion

58:00

Computational Creativity Lecture 15: Large language models and their implications

01:04:49

Computational Creativity Lecture 14: Attention and transformers

01:04:43

Computational Creativity Lecture 13: Neural language models and word embeddings

46:01

Computational Creativity Lecture 12: Normalizing flow models

57:04

Computational Creativity Lecture 11: Denoising diffusion models

47:49

Computational Creativity Lecture 10: DeepDream and neural style transfer

52:12

Computational Creativity Lecture 9: Image-to-Image GANs and GAN artists

01:00:19

Computational Creativity Lecture 8: Advanced GANs

56:10

Computational Creativity Lecture 7: Generative Adversarial Networks (GANs)

01:07:09

Computational Creativity Lecture 6: VQ-VAEs and image quality metrics

01:05:30

Computational Creativity Lecture 5: Variational autoencoders

01:07:25

Computational Creativity Lecture 4: Deep Learning Crash Course

58:42

Computational Creativity Lecture 3: Probability and machine learning review

53:31

Computational Creativity Lecture 2: Algorithms for Making Art (~1960-2010)

01:00:40

Computational Creativity Lecture 1: Introduction to Generative Models

02:05

Computer Vision for Visual Effects 2021

00:09

Attack the Broc 2: It Moves

00:09

Attack the Broc

02:46

Electrical, Computer, and Systems Engineering at Rensselaer

06:22

PB19: The Poisson Random Variable

13:58

PB 5: Combinatorics

12:09

PB45: The Joint Gaussian Random Variable

09:30

PB50: Class-Conditional Probability Density Functions

12:18

PB53: Conditional Gaussian Distributions

07:53

PB51: The Bayes Decision Rule

07:35

PB59: The PDF of a Sum of Random Variables

09:34

PB70: Hypothesis Testing

05:53

PB63: Weak Law of Large Numbers vs. Central Limit Theorem

09:57

PB62: Central Limit Theorem Practice Problems

10:12

PB58: Laws of Large Numbers

06:29

PB46: Independence of Random Variables

10:16

PB56: More Conditional Expectation Practice Problems

06:34

PB54: The Law of Iterated Expectation

10:56

PB60: Transformations of Random Variables

12:18

PB49: Conditional PMFs for Discrete Random Variables

06:04

PB72: Testing the Fit of a Distribution

07:41

PB73: Generating Samples of a Random Variable

10:29

PB48: The Correlation Coefficient

06:49

PB55: Conditional Expectation Practice Problems

08:08

PB64: Confidence Intervals

08:18

PB74: Tips and Tricks for Random Number Generation

12:19

PB47: Joint Expectations and Covariance

09:57

PB69: Significance Testing

07:05

PB71: A Hypothesis Testing Example

09:34

PB65: Maximum A Posteriori (MAP) Estimation