Главная
Study mode:
on
1
Introduction to Machine Learning
2
Week 1 - Lecture 1 - Introduction to Machine Learning
3
Week 1 Lecture 2 - Supervised Learning
4
Week 1 Lecture 3 - Unsupervised Learning
5
Week 1 Lecture 4 - Reinforcement Learning
6
Week 2 Lecture 5 - Statistical Decision Theory - Regression
7
Week 2 Lecture 6 - Statistical Decision Theory - Classification
8
Week 2 Lecture 7 - Bias - Variance
9
Week 2 Lecture 8 - Linear Regression
10
Week 2 Lecture 9 - Multivariate Regression
11
Week 3 Lecture 10 Subset Selection 1
12
Week 3 Lecture 11 Subset Selection 2
13
Week 3 Lecture 12 Shrinkage Methods
14
Week 3 Lecture 13 Principal Components Regression
15
Week 3 Lecture 14 Partial Least Squares
16
Week 3 Lecture 15 Linear Classification
17
Week 3 Lecture 16 Logistic Regression
18
Week 3 Lecture 17 Linear Discriminant Analysis 1
19
Week 3 Lecture 18 Linear Discriminant Analysis 2
20
Week 3 Lecture 19 Linear Discriminant Analysis 3
21
Week 4 Lecture 20 Perceptron Learning
22
Week 4 Lecture 21 SVM - Formulation
23
Week 4 Lecture 22 SVM - Interpretation & Analysis
24
Week 4 Lecture 23 SVMs for Linearly Non Separable Data
25
Week 4 Lecture 24 SVM Kernels
26
Week 4 Lecture 25 SVM - Hinge Loss Formulation
27
Week 5 Lecture 26 ANN I - Early Models
28
Week 5 Lecture 27 ANN II - Backprogpogation I
29
Week 5 Lecture 28 ANN III - Backpropogation II
30
Week 5 Lecture 29 ANN IV - Initialization, Training & Validation
31
MAXIMUM LIKELIHOOD ESTIMATE
32
Week 5 Lecture 31 Parameter Estimation II - Priors & MAP
33
Week 5 Lecture 32 Parameter Estimation III - Bayesian Estimation
34
Week 6 Lecture 33 Decision Trees - Introduction
35
Week 6 Lecture 34 Regression Trees
36
Week 6 Lecture 35 Stopping Criteria & Pruning
37
Week 6 Lecture 36 Decision Trees for Classification - Loss Functions
38
Week 6 Lecture 37 Decision Trees - Categorical Attributes
39
Week 6 Lecture 38 Decision Trees - Multiway Splits
40
Week 6 Lecture 39 Decision Trees - Missing Values, Imputation & Surrogate Splits
41
Week 6 Lecture 40 Decision Trees - Instability, Smoothness & Repeated Subtrees
42
Week 6 Lecture 41 Decision Trees - Example
43
Week 6 Lecture 42 Evaluation Measures 1
44
Week 6 Lecture 43 Bootstrapping & Cross Validation
45
Week 6 Lecture 44 - 2 Class Evaluation Measures
46
Week 6 Lecture 45 - The ROC Curve
47
Week 6 Lecture 46 - Minimum Description Length & Exploratory Analysis
48
Week 7 Lecture 47 - Introduction to Hypothesis Testing
49
Week 7 Lecture 48 - Basic Concepts
50
Week 7 Lecture 49 - Hypothesis Testing II - Sampling Distributions & The Z test
51
Week 7 Lecture 50 -STUDENT'S T-TEST
52
Week 7 Lecture 51 - Hypothesis Testing IV - The Two Sample and Paired Sample t-tests
53
Week 7 Lecture 52 - Confidence Intervals
54
Week 8 Lecture 53 - Ensemble Methods - Bagging, Committee Machines and Stacking
55
Week 8 Lecture 54 - Boosting
56
Week 8 Lecture 55 - Gradient Boosting
57
Week 8 Lecture 56 - Random Forests
58
Week 8 Lecture 57 - Naive Bayes
59
Week 9 Lecture 58 Bayesian Networks
60
Week 9 Lecture 59 Undirected Graphical Models - Introduction
61
Week 8 Lecture 60 Undirected Graphical Models - Potential Functions
62
Week 9 Lecture 61 Hidden Markov Models
63
Week 9 Lecture 62 Variable Elimination
64
Week 9 Lecture 63 Belief Propagation
65
Lecture 64 Multi-class Classification
66
Week 10 Lecture 65 Partional Clustering
67
Week 10 Lecture 66 Hierarchical Clustering
68
Week 10 Lecture 67 Threshold Graphs
69
Week 10 Lecture 68 The BIRCH Algorithm
70
Week 10 Lecture 69 The CURE Algorithm
71
Week 10 Lecture 70 Density Based Clustering
72
Week 11 Lecture 71 Gaussian Mixture Models
73
Week 11 Lecture 72 Expectation Maximization
74
Week 11 Lecture 73 Expectation Maximization Continued
75
Lecture 76 Spectral Clustering
76
The Apriori Property
77
Frequent Itemset Mining
78
Lecture 79 Learning Theory
79
Lecture 80 Introduction to Reinforcement Learning
80
Lecture 81 - RL Framework and TD Learning
81
Lecture 82 Solution Methods & Applications
82
Week 6 Decision Trees Tutorial
83
Week 4 Tutorial 4 - Optimization
84
Week 3 Weka Tutorial
85
Week 2 Tutorial 2 - Linear Algebra (2)
86
Week 2 Tutorial 2 - Linear Algebra (1)
87
Week 1 Tutorial 1 - Probability Basics (2)
88
Week 1 Tutorial 1 - Probability Basics (1)
Description:
With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.

Machine Learning

Indian Institute of Technology Madras
Add to list
#Computer Science #Machine Learning #Supervised Learning #Unsupervised Learning #Artificial Intelligence #Neural Networks
0:00 / 0:00
1 of 88

Introduction to Machine Learning