# In-depth introduction to machine learning in 15 hours of expert videos

#### R-bloggers 2014-09-23

### Summary:

In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, **An Introduction to Statistical Learning with Applications in R (ISLR)**. I found it to be an excellent course in statistical learning (also known as "machine learning"), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book.

**If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover** to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors' website.

If you decide to attempt the exercises at the end of each chapter, there is a GitHub repository of solutions provided by students you can use to check your work.

**As a supplement to the textbook, you may also want to watch the excellent course lecture videos** (linked below), in which Dr. Hastie and Dr. Tibshirani discuss much of the material. In case you want to browse the lecture content, I've also linked to the PDF slides used in the videos.

### Chapter 1: Introduction (slides, playlist)

- Opening Remarks and Examples (18:18)
- Supervised and Unsupervised Learning (12:12)

### Chapter 2: Statistical Learning (slides, playlist)

- Statistical Learning and Regression (11:41)
- Curse of Dimensionality and Parametric Models (11:40)
- Assessing Model Accuracy and Bias-Variance Trade-off (10:04)
- Classification Problems and K-Nearest Neighbors (15:37)
- Lab: Introduction to R (14:12)

### Chapter 3: Linear Regression (slides, playlist)

- Simple Linear Regression and Confidence Intervals (13:01)
- Hypothesis Testing (8:24)
- Multiple Linear Regression and Interpreting Regression Coefficients (15:38)
- Model Selection and Qualitative Predictors (14:51)
- Interactions and Nonlinearity (14:16)
- Lab: Linear Regression (22:10)

### Chapter 4: Classification (slides, playlist)

- Introduction to Classification (10:25)
- Logistic Regression and Maximum Likelihood (9:07)
- Multivariate Logistic Regression and Confounding (9:53)
- Case-Control Sampling and Multiclass Logistic Regression (7:28)
- Linear Discriminant Analysis and Bayes Theorem (7:12)
- Univariate Linear Discriminant Analysis (7:37)
- Multivariate Linear Discriminant Analysis and ROC Curves (17:42)
- Quadrati