Introduction to Statistical Machine Learning
Description
The first part of his tutorial provides a brief overview of the fundamental methods
and applications of statistical machine learning.
The other speakers will detail or built upon this introduction. Statistical machine learning is concerned with
the development of algorithms and techniques that learn from
observed data by constructing stochastic models that can be used for
making predictions and decisions. Topics covered include Bayesian inference and maximum likelihood
modeling; regression, classification, density estimation,
clustering, principal component analysis; parametric,
semi-parametric, and non-parametric models; basis functions, neural
networks, kernel methods, and graphical models; deterministic and
stochastic optimization; overfitting, regularization, and
validation.
| Slides | |
| 0:00 | Introduction to Statistical Machine Learning |
| 0:32 | Abstract |
| 0:34 | Table of Contents |
| 1:27 | 1 INTRO/OVERVIEW/PRELIMINARIES |
| 2:04 | What is Machine Learning? |
| 3:42 | Why 'Learn'? |
| 5:49 | Handwritten Character Recognition |
| 6:32 | Applications of Machine Learning |
| 8:20 | Some Fundamental Types of Learning |
| 10:36 | Supervised Learning |
| 11:05 | Classification |
| 12:16 | Regression |
| 12:56 | Unsupervised Learning |
| 13:12 | Reinforcement Learning |
| 15:27 | Dichotomies in Machine Learning |
| 20:13 | Probability Basics |
| 22:06 | Probability Jargon |
| 29:16 | 2 LINEAR METHODS FOR REGRESSION |
| 30:06 | Linear Regression |
| 32:14 | Coefficient Subset Selection |
| 33:19 | Coefficient Shrinkage |
| 34:40 | Linear Methods for Classification |
| 38:34 | Linear Basis Function Regression (LBFR) (1) |
| 42:03 | Linear Basis Function Regression (LBFR) (2) |
| 42:11 | 2D Spline LBFR and 1D Symmlet-8 Wavelets |
| 42:54 | Local Smoothing & Kernel Regression |
| 44:16 | Regularization & 1D Smoothing Splines |
| 46:29 | 3 NONLINEAR REGRESSION |
| 46:47 | Artificial Neural Networks 1 |
| 48:50 | Artificial Neural Networks 2 |
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32 comments
Watch videos: (click on thumbnail to launch)
Part 1
0:51:09
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Part 2
0:54:36
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Part 3
0:40:08
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I think part 2 finishes about 14 min.
Thnx for your comment. We have replaced the old part 2 with a new video.
Perhaps an irrelevant comment about the last slide in part 2: in the example with the cube packing the dimension, at which the central spere sticks out of the cube, seems to be d=10, not d=11. For d=9 the sphere touches the cube faces (follows from (sqrt(d)-1)/2=1).