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更新日期：2019-04-04
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★ Table of Contents ★
1) Course Introduction
2) Introduction
3) Course Overview-Part 1
4) Course Overview-Part 2
5) Graphical Approaches
6) Measures of Central Tendency
7) Measures of Dispersion
8) Random Variables and Probability Distribution
9) Probability Distributions-Part 1
10) Probability Distributions-Part 2
11) Inferential Statistics - Motivation
12) Inferential Statistics - Single sample tests
13) Two Sample tests
14) Type 1 and Type 2 Errors
15) Confidence Intervals
16) ANOVA and Test of Independence
17) Short Introduction to Regression
18) Introduction to Machine Learning
19) Supervised Learning
20) Unsupervised Learning
21) Simple and Multiple Regression
22) Regularization/ Coefficients Shrinkage
23) Data Modelling and Algorithmic Modelling
24) Ordinary Least Squares Regression
25) NPTEL MOOC IDA - Tutorial for Assignment 2
26) Training a Logistic Regression Classifier
27) Logistic Regression
28) Classification and Regression Trees-Part 1
29) Classification and Regression Trees-Part 2
30) Bias Variance Dichotomy
31) Support Vector Machines-Part 1
32) Support Vector Machines-Part 2
33) Support Vector Machines-Part 3
34) Support Vector Machines-Part 4
35) Model Assessment and Selection
36) NPTEL MOOC IDA Tutorial for Assignment 3
37) Deep Learning
38) Artificial Neural Networks-Part 1
39) Ensemble Methods and Random Forests
40) Artificial Neural Networks-Part 2
41) Associative Rule Mining-Part 1
42) Association Rule Mining-Part 2
43) Big Data, A small introduction-Part 1
44) Clustering Analysis-Part 1
45) Big Data - A small introduction-Part 2
46) Reinforcement Learning-Part 1
47) Reinforcement Learning-Part 2
48) Clustering Analysis-Part 2
49) Experimentation and Active Learning-Part 1
50) Experimentation and Active Learning-Part 2
51) Summary+ Insights into the Final Exam
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