https://github.com/abbyazari/umich-eecs545-lectures

This repository contains the lecture materials for EECS 545, a graduate course in Machine Learning, at the University of Michigan, Ann Arbor.

https://github.com/abbyazari/umich-eecs545-lectures

Repository

This repository contains the lecture materials for EECS 545, a graduate course in Machine Learning, at the University of Michigan, Ann Arbor.

https://github.com/abbyazari/umich-eecs545-lectures/blob/master/

# EECS 545, Winter 2016

This repository contains the lecture materials for EECS 545, a graduate course in Machine Learning, at the University of Michigan, Ann Arbor. 

## [Formatted Lecture Materials](./Lectures.md)

The link above gives a list of all of the available lecture materials, including links to ipython notebooks (via [Jupyter's nbviewer](http://nbviewer.jupyter.org/)), the slideshow view, and PDFs.

## Lecture Readings

We will make references to the following textbooks throughout the course.  The only required textbook is Bishop, *PRML*, but the others are very well-written and offer unique perspectives.

- Bishop 2006, [*Pattern Recognition and Machine Learning*](http://research.microsoft.com/en-us/um/people/cmbishop/prml/)
- Murphy 2012, [*Machine Learning:  A Probabilistic Perspective*](https://www.cs.ubc.ca/~murphyk/MLbook/)

#### Lecture 01:  Introduction to Machine Learning
*Wednesday, January 6, 2016*

No required reading.

#### Lecture 02:  Linear Algebra & Optimization
*Monday, January 11, 2016* 

- There are lots of places to look online for linear algebra help!
- Juan Klopper has a [nice online review](http://www.juanklopper.com/opencourseware/mathematics-2/ipython-lecture-notes/), based on Jupyter notebooks.

#### Lecture 03:  Convex Functions & Probability
*Wednesday, January 13, 2016*   ([Notebook Viewer](http://nbviewer.jupyter.org/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture03_convex-functions-optimization/lecture03_Convex-Functions-and-Optimization.ipynb), [PDF File](https://github.com/thejakeyboy/umich-eecs545-lectures/raw/master/lecture03_convex-functions-optimization/lecture03_Convex-Functions-and-Optimization.pdf), [Slide Viewer](http://nbviewer.jupyter.org/format/slides/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture03_convex-functions-optimization/lecture03_Convex-Functions-and-Optimization.ipynb))

Required:
- **Bishop, 1.2:** Probability Theory
- **Bishop, 2.1-2.3:** Binary, Multinomial, and Normal Random Variables

Optional:
- **Murphy, Chapter 2:**  Probability

#### Lecture 04:  Linear Regression, Part I
*Wednesday, January 20, 2016*   ([Notebook Viewer](http://nbviewer.jupyter.org/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture04_linear-regression-part1/lecture04_final_linear-regression-part-1.ipynb), [PDF File](https://github.com/thejakeyboy/umich-eecs545-lectures/raw/master/lecture04_linear-regression-part1/lecture04_final_linear-regression-part-1.pdf), [Slide Viewer](http://nbviewer.jupyter.org/format/slides/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture04_linear-regression-part1/lecture04_final_linear-regression-part-1.ipynb))

Required:
- **Bishop, 1.1:**  Polynomial Curve Fitting Example
- **Bishop, 3.1:**  Linear Basis Function Models

Optional:
- **Murphy, Chapter 7:**  Linear Regression

#### Lecture 05:  Linear Regression, Part II
*Monday, January 25, 2016*   ([Notebook Viewer](http://nbviewer.jupyter.org/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture05_linear-regression-part2/lecture05_final_linear-regression-part-2.ipynb), [PDF File](https://github.com/thejakeyboy/umich-eecs545-lectures/raw/master/lecture05_linear-regression-part2/lecture05_final_linear-regression-part-2.pdf), [Slide Viewer](http://nbviewer.jupyter.org/format/slides/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture05_linear-regression-part2/lecture05_final_linear-regression-part-2.ipynb))

Required:
- **Bishop, 3.2:**  The Bias-Variance Decomposition
- **Bishop, 3.3:**  Bayesian Linear Regression

Optional:
- **Murphy, Chapter 7:**  Linear Regression

#### Lecture 06:  Probabilistic Models & Logistic Regression
*Wednesday, January 27, 2016*   ([Notebook Viewer](http://nbviewer.jupyter.org/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture06_generative-models-gda/lecture06_final_probability-models-logistic-regression.ipynb), [PDF File](https://github.com/thejakeyboy/umich-eecs545-lectures/raw/master/lecture06_generative-models-gda/lecture06_final_probability-models-logistic-regression.pdf), [Slide Viewer](http://nbviewer.jupyter.org/format/slides/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture06_generative-models-gda/lecture06_final_probability-models-logistic-regression.ipynb))

Required:
- **Bishop, 4.2:**  Probabilistic Generative Models
- **Bishop, 4.3:**  Probabilistic Discriminative Models

Optional:
- **Murphy, Chapter 8:**  Logistic Regression

#### Lecture 07:  Linear Classifiers
*Monday, February 1, 2016*   ([Notebook Viewer](http://nbviewer.jupyter.org/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture07_nb-lda-perceptron/lecture07_final_nb-gda-lda.ipynb), [PDF File](https://github.com/thejakeyboy/umich-eecs545-lectures/raw/master/lecture07_nb-lda-perceptron/lecture07_final_nb-gda-lda.pdf), [Slide Viewer](http://nbviewer.jupyter.org/format/slides/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture07_nb-lda-perceptron/lecture07_final_nb-gda-lda.ipynb))

Required:
- **Bishop, 4.1:**  Discriminant Functions

Recommended:
- **Murphy 3.5:**  Naive Bayes Classifiers
- **Murphy 4.1:**  Gaussian Models
- **Murphy 4.2:**  Gaussian Discriminant Analysis

Optional:
- **CS 229:** Notes on [Generative Models](http://cs229.stanford.edu/notes/cs229-notes2.pdf)
- **Paper:**  Zhang, H., 2004. ["The optimality of naive Bayes"](http://www.cs.unb.ca/profs/hzhang/publications/FLAIRS04ZhangH.pdf). AA, 1(2), p.3.
- **Paper:**  Domingos, P. and Pazzani, M., 1997. ["On the optimality of the simple Bayesian classifier under zero-one loss"](http://link.springer.com/article/10.1023/A:1007413511361). Machine learning, 29(2-3), pp.103-130.

#### Lecture 08:  Kernel Methods I,  Kernels
*Monday, February 8, 2016* 

Required:
- **Bishop, 6.1:**  Dual Representation
- **Bishop, 6.2:**  Constructing Kernels
- **Bishop, 6.3:**  Radial Basis Function Networks

Optional:
- **Murphy, 14.2:**  Kernel Functions

#### Lecture 09:  Kernel Methods II, Support Vector Machines
*Wednesday, February 10, 2016* 

Required:
- **Bishop, 6.1:**  Dual Representation
- **Bishop, 6.3:**  Radial Basis Function Networks
- **Bishop, 7.1:**  Maximum Margin Classifiers

Optional:
- CS229:  [Support Vector Machines](http://cs229.stanford.edu/notes/cs229-notes3.pdf)
- Eric Kim, [Everything You Wanted to Know about the Kernel Trick (But were too Afraid to Ask)](http://www.eric-kim.net/eric-kim-net/posts/1/kernel_trick.html)

#### Lecture 10:  Kernel Methods III, Bayesian Linear Regression & Gaussian Processes
*Monday, February 15, 2016* 

Required:
- **Bishop, 2.3.0-2.3.1:**  Gaussian Distributions
- **Bishop, 3.3:**  Bayesian Linear Regression
- **Bishop, 6.4:**  Gaussian Processes

Recommended:
- **Murphy, 7.6.1-7.6.2:**  Bayesian Linear Regression
- **Murphy, 4.3:**  Inference in Joinly Gaussian Distributions

Further Reading:
- **Rasmussen & Williams**, Gaussian Processes for Machine Learning.  (available [free online](http://www.gaussianprocess.org/gpml/))

#### Lecture 11:  Machine Learning Advice
*Wednesday, February 17, 2016*

No required reading.

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