**Note: Lecture slides are best viewed in Chrome.**

**Machine Learning**

Dates | Topics | Lecture Slides with Matlab | Python Code | HW |

03/03/2016 | Introduction | Slide#01 | ||

03/08/2016 | Linear Algebra 1 Linear Algebra 2 |
Slide#02 Slide#03 |
||

03/10/2016 | Linear Algebra 3 Optimization 1 |
Slide#04 Slide#05 |
||

03/15/2016 | Optimization 2 | Slide#06 | ||

03/17/2016 | Graph Regression 1 | Slide#07 Slide#08 |
||

03/22/2016 | Regression 1 | Slide#08 | ||

03/24/2016 | Regression 2 Regression 3 |
Slide#09 Slide#10 |
||

03/29/2016 | Classification: Perceptron | Slide#11 | ||

03/29/2016 | SVM | Slide#12 | ||

04/05/2016 | Logistic Regression | Slide#13 | HW#01 (Solution#01) | |

04/07/2016 | Clustering: K-means | Slide#14 | ||

04/12/2016 | Statistics | Slide#15 | HW#02 (Solution#02) | |

04/14/2016 | Monte Carlo simulation | Slide#16 | ||

04/19/2016 | PCA | Slide#17 | ||

04/22/2016 | exam | Exam (Solution) | ||

04/26/2016 | Fisher | Slide#18 | ||

05/03/2016 | SVD | Slide#19 | HW#03 (Solution#03) | |

05/10/2016 | ICA | Slide#20 | ||

05/12/2016 | Network | Slide#21 | ||

05/17/2016 | Probability | Slide#22 | ||

05/26/2016 | Gaussian Distribution | Slide#23 | ||

05/31/2016 | Parameter Estimation | Slide#24 | HW#04 (Solution#04) | |

06/02/2016 | Probabilistic Machine Learning | Slide#25 | ||

06/07/2016 | Bayesian Machine Learning | Slide#26 Slide#27 |
||

Gaussian Process | Slide#28 | |||

Kalman Filter | Slide#29 | |||

06/14/2016 | exam | Exam |

**Deep Learning**

**Short Course for Industry**

Dates | Topics | Lecture Slides with python | KSNVE | |

Introduction | Slide#00 | Slide#00 | ||

Supervised Learning | Slide#01 | Slide#01 | ||

Unsupervised Learning | Slide#02 | Slide#02 | ||

Neural Network | Slide#03 | Slide#03 | ||

Convolutional Neural Network | Slide#04 | Slide#04 | ||

Recurrent Neural Network | Slide#05 | Slide#05 | ||

Hands-on Practice | Slide#06 | Slide#06 |