10-701 Machine Learning Fall 2014 - Lecture 1

Показать описание

Topics: course logistics, high-level overview of machine learning, classification
Lecturer: Aarti Singh

Рекомендации по теме

Комментарии

1. Lecture 1: Course logistics, high-level overview of machine learning,
classification - Aarti Singh
2. Lecture 2: Classification, Naive Bayes, Introduction to MLE and MAP
Estimations - Aarti Singh
3. Recitation 1: Review of probability theory, Multivariate normal
distribution - Ben Cowley
4. Lecture 3: Perceptron, Linear Programming, "Perceptron algorithm" -
Geoff Gordon
5. Lecture 4: Logistic regression, generative vs discriminative
classifiers, analysis of perceptron algorithm - Aarti Singh and Geoff Gordon
6. Recitation 2: bag of words, MLE and MAP - Nicole Rafidi
7. Lecture 5: Analysis of perceptron algorithm (separable and non-separable),
amortized analysis - Geoff Gordon
8. Lecture 6: reproducing kernel Hilbert space, kernel perceptron algorithm
and analysis - Geoff Gordon
9. Recitation 3: introduction to optimization and convexity, gradient
descent, backgracking line search - Anthony Platanios
10. Lecture 7: kernel perceptron, kernel engineering, support vector machine
(SVM) - Geoff Gordon
11. Lecture 8: linear regression, least squares, polynomial regression - Aarti
Singh
12. Recitation 4: support vector machine (SVM) primal and dual problems, soft
margin SVM, kernel trick, kernel selection - Zichao Yang
13. Lecture 9: Polynomial regression, kernelized regression, Gaussian process
(GP) regression - Aarti Singh
14. Lecture 10: optimization, gradient descent, Newton's method, convergence
analysis - Geoff Gordon
15. Recitation 5: kernel methods, kernel trick, intuition behind RKHS - Adona
losif
16. Midterm review
17. Lecture 11: Newton method, backtracking line search, constrained
optimization, stochastic gradient descent, density estimation - Geoff Gordon and Aarti Singh
18. Lecture 12: kernel density estimation, k-nearest neighbors, local regression, introduction to spatially
adaptive nonparametric methods - Aarti Singh
19. Lecture 13: training decision trees, pruning, regression trees, boosting - Aarti Singh
20. Recitation 6: regularized regression, kernel regression, Gaussian processes, bias-variance tradeoff -
Nicole Rafidi
21. Lecture 14: analysis of boosting, introduction to graphical models - Aarti Singh and Geoff Gordon
22. Lecture 15: graphical models, variable elimination, Bayesian networks, independent relations in graphical
models - Geoff Gordon
23. Recitation 7: Practice working with probability distributions involving linear algebra and matrix calculus
- Anthony Platanios
24. Lecture 16: d-separation, Bayes ball algorithm, factor graphs, Markov random fields - Geoff Gordon
25. Lecture 17: Hidden Markov Model (HMM), belief propagation, junction tree algorithm - Geoff Gordon
26. Recitation 8: probabilistic modeling, graphical models, Gaussian mixture models, expectation maximization
(EM) - Abu Saparov
27. Lecture 18: plate notation in graphical models, introduction to learning theory, probably approximately
correct (PAC) learning - Geoff Gordon and Aarti Singh
28. error bounds for infinite hypothesis spaces, Vapnik-Chervonenkis (VC) dimension, Rademacher complexity -
Aarti Singh
29. Recitation 9: review of d-separation, probably approximately correct(PAC) bounds, Vapnik-Chervonenkis (VC)
dimension - Ben Cowley
30. Midterm 2 review - Ben Cowley
31. Lecture 20: clustering, hierarchical clustering methods, k-means, mixture of Gaussians - Aarti Singh
32. Recitation 10: Hidden Markov Models, forward-backward algorithm, Viterbi algorithm for finding the most
probable state sequence, EM for HMMs - Zichao Yang
33. Lecture 21: expectation maximization (EM), convergence of EM, principal component analysis (PCA) - Aarti
Singh
34. Lecture 22: principal component analysis (PCA), deep learning/networks, neural networks - Aarti Singh and
Geoff Gordon
35. Lecture 23: Deep learning, backpropagation for training neural networks, very brief introduction to
spectral methods - Geoff Gordon

ytuber

Pre-requisites
Linear Algebra
Basic Probability and Statistics
Multivariate Calculus

anjalibhatt

10-701 Machine Learning Fall 2014 - Lecture 1

10-701 Machine Learning Fall 2014 - Lecture 1

10-701 Machine Learning Fall 2014 - Recitation 1

10-701 Machine Learning Fall 2014 - Recitation 5

10-701 Machine Learning Fall 2014 - Lecture 14

10-701 Machine Learning Fall 2014 - Midterm review

10-701 Machine Learning Fall 2014 - Lecture 10

10-701 Machine Learning Fall 2014 - Recitation 10

10-701 Machine Learning Fall 2014 - Recitation 2

10-701 Machine Learning Fall 2014 - Recitation 4

10-701 Machine Learning Fall 2014 - Lecture 22

10-701 Machine Learning Fall 2014 - Lecture 11

10-701 Machine Learning Fall 2014 - Lecture 13

10-701 Machine Learning Fall 2014 - Recitation 3

10-701 Machine Learning Fall 2014 - Recitation 6

10-701 Machine Learning Fall 2014 - Lecture 4

10-701 Machine Learning Fall 2014 - Lecture 2

10-701 Machine Learning Fall 2014 - Lecture 12

10-701 Machine Learning Fall 2014 - Lecture 15

10-701 Machine Learning Fall 2014 - Recitation 8

10-701 Machine Learning Fall 2014 - Lecture 21

10-701 Machine Learning Fall 2014 - Lecture 20

10-701 Machine Learning Fall 2014 - Recitation 9

10-701 Machine Learning Fall 2014 - Recitation 7

10-701 Machine Learning Fall 2014 - Lecture 8