**Lecture: ** 11:30am–1:00pm, Tuesday–Thursday (FH215)**
Office hours:** 1:30pm–2:30pm, Tuesday–Thursday (W308, Engineering Building 2)

**TA:**Ilker Gurcan, TA’s Email: igurcan@central.uh.edu

**References:**

[1] The Elements of Statistical Learning, Data Mining, Inference, and Prediction by Trevor Hastie &

[2] Machine learning: A Probabilistic Perspective by Kevin P. Murphy

[3] Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

[4] Reinforcement learning: An introduction by Richard S. Sutton and Andrew G. Barto

**Mathematical background:
**[1] Linear Algebra and Its Applications by Gilbert Strang

[2] Matrix Computations by Gene H. Golub and Charles F. Van Loan

**Syllabus:
**

Date |
Lecture |
Suggested reading |
Assignment |

22-Aug | Linear Model (slides) | 7.1–7.3 Murphy | |

5-Sept | Perceptron (slides) | 4.5.1 Hastie | |

7-Sept | Regularized linear Model (Ridge, Lasso) (slides) (bias-variance note) | 3.4.1–3.4.2 Hastie | |

12-Sept | Introduction to neural networks (Slides) | 6.1-6.5 Goodfellow | |

14-Sep | Stochastic optimization algorithms I (Slides) | 8.3 Goodfellow | 1 (pdf) |

19-Sep | Stochastic optimization algorithms II (slides) | 8.3 Goodfellow | |

21-Sep | Support vector machine | 14.5 Murphy | |

26-Sep | Support vector machine (slides) | 14.5 Murphy | 2 (pdf) (answer) |

28-Sep | Kernel methods | 14.7 Murphy | |

3-Oct | Decision trees, random forests (slides) | 9.2.2 & 15 Hastie | 5 |

5-Oct | Boosting | 10 Hastie | |

10-Oct | Boosting | 10 Hastie | 6 |

12-Oct | Midterm | ||

Principal Component Analysis, Laplacian Eigenmaps | Belkin & Niyogi 2002 | ||

Graph embedding – General framework of dimensionality reduction (Slides) | Shuicheng Yan 2007 | 7 | |

Sparse representation (Slides) | Lecturer’s slides | ||

Clustering (K-means, Spectral) (slides) | Lecturer’s slides | 8 | |

Generative adversarial networks (Slides) | Goodfellow et al. 2014 | 9 | |

Convolutional neural networks | LeCun & Bengio 1995 | ||

Convolutional neural networks (Slides) | LeCun & Bengio 1995 | 10 | |

Recurrent networks | 10.1 Goodfellow | ||

Long short term memory network (Slides) | Hochreiter et al. 1997 | 11 | |

Visualization of Deep Networks (Slides) | Lecturer’s slides | ||

Visualization of Deep Networks | Lecturer’s slides | 12 | |

Reinforcement learning I (Slides) | 4.1-4.4 Sutton 2006 | ||

Reinforcement learning II (Slides) (ppt) | 4.1-4.4 Sutton 2006 | ||

Reinforcement learning III (Slides) (ppt) | 4.1-4.4 Sutton 2006 | ||

Final project presentation (Description) |

**TA Lectures:
**Introduction to Python and TensorFlow 1 (slides) (codes)

**Online Tensorflow environment **(for testing python and tensorflow codes)

https://codeenv.com/env/codeenv/14/tensor-flow/

hvnguyenPost authorPost your questions here!

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