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