#### DS351 | Statistical Learning for Data Science 1

#### Spring 2022

#### [Syllabus] [kaggle report (one-column)] [kaggle report (two-column)]

#### Time

- Sec 01 : M 11:00-13:00 in Microsoft Teams

#### Instructors

- Donlapark Ponnoprat (ดลภาค พรนพรัตน์)

E-mail: donlapark.p--a--cmu.ac.th

Office: STB304

#### References

- [ISLR] Trevor Hastie, Robert Tibshirani, Daniela Witten and Gareth James,
*An Introduction to Statistical Learning: with Applications in R*[book] [python code] - [FPP] Rob J Hyndman and George Athanasopoulos,
*Forecasting: Principles and Practice*[online book] - [LAML] Charu C Aggarwal,
*Linear Algebra and Optimization for Machine Learning*

#### Announcements

- Homework 1 is due on Jan 5th
- Homework 2 is due on Jan 22nd

#### Lecture Notes

Date | Topic | Notes/HW | Labs | Readings |
---|---|---|---|---|

Nov 23 | Introduction | Lecture 1 | Lab 1 | ISLR 2.1.5 |

Nov 30 | Linear algebra |
Lecture 2 Homework 1 |
Lab 2 | LAML 1.2.1-1.2.4, 2.3.1-2.3.2 |

Dec 14 | Principal component analysis (PCA) | Lecture 3 | Lab 3-1 Lab 3-2 |
ISLR 10.2.1-10.2.2 LAML 3.3, 3.3.7 |

Dec 21 | Bias-Variance tradeoff | Lecture 4 | Lab 4 | ISLR 2.2.1-2.2.2 |

Jan 4 | Linear regression I |
Lecture 5
Homework 2 [Carseats data] |
Lab 5 | ISLR 3.1 |

Jan 11 | Linear regression II |
Lecture 6 |
Lab 6 | ISLR 3.2 |

Jan 18 | Linear regression III | Lecture 7 | Lab 7 | ISLR 3.3 |

Jan 25 | Midterm week | |||

Feb 1 | Time series I | Lecture 8 Homework 3 | Lab 8-1 Lab 8-2 | FPP 2.6-2.8, 8.1 |

Feb 8 | Time series II | Lecture 9 | Lab 9 | FPP 6.1-6.3, 6.7, 6.8 |

Feb 15 | Time series III | Lab 10-1 Lab 10-2 | FPP 7.1-7.4, 7.6 | |

Feb 22 | Time series IV | Lecture 10 | Lab 11-1 Lab 11-2 | FPP 8.1-8.6, 8.8 |

Mar 1 | Logistic regression I | Lecture 11 Homework 4 | Lab 12 | ISLR 4.2, 4.3.1-4.3.3 |

Mar 8 | Linear discriminant analysis & Model evaluation | Lecture 12 | Lab 13 | ISLR 4.3.4-4.3.5, |

Mar 15 | Final review |