**When**: 3rd quartile, at $8$ weeks of $4$ hours per week.**Load**: Total workload is 5 ECTS $\Rightarrow 5\times 28 \text{[hrs/ECTS]} = 140$ hours or $140/32 \approx 4.4$ study hours per lecture.**Web**- home at http://bmlip.nl (or goto teaching tab at http://biaslab.org )
- source materials at github repo at https://github.com/bertdv/BMLIP
- Please file a github issue if something is wrong or just unclear in these notes.

**Instructors**- Bert de Vries, rm. FLUX-7.101 (on Wednesdays), responsible for full course
- Wouter Kouw, rm. FLUX-7.060, responsible for Probabilistic Programming mini-course
- Teaching assistents: Magnus Koudahl and Ismail Senoz, rm. FLUX-7.060
- Please contact the TA's first for any questions regarding Julia programming examples and exercises

Suppose you need to develop an algorithm for a complex DSP task, e.g., a speech recognition engine. This is what you'll do:

- Choose a set of candidate algorithms $y=H_k(x;\theta)$ where $k \in \{1,2,\ldots,K\}$ and $\theta \in \Theta_k$; (you think that) there's an algorithm $H_{k^*}(x;\theta^*)$ that performs according to your liking.
- You collect a set of examples $D=\{(x_1,y_1),(x_2,y_2),\ldots,(x_N,y_N)\}$ that are consistent with the correct algorithm behavior.

- Using the methods from this class, you will be able to design a suitable algorithm through learning from the data set, thus achieving:
**model selection**, i.e., find $k^*$**parameter estimation**, i.e., find $\theta^*$

- Better yet, we will discuss methods that find distributions $p(k|D)$ and $p(\theta|D)$ that represent your knowledge about the best models and parameters, given the data set.

Book (free download link):

Mostly used for background reading as the (mandatory) slides are the main resource.

- Book theme: Whatever you do in machine learning, you can do it better with Bayesian methods.
- Contains much more material; great for future study and reference.

- Tested material consists of these lecture notes, reading assignments (as assigned in the first cell/slide of each lecture notebook) and exercises (see class website).

- Advice: Make Exercises, (to be) posted and regularly updated on the course website.

- Advice: download (and make free use of) Sam Roweis' cheat sheets for Matrix identities and Gaussian identities.

- You are not allowed to use books nor bring printed or handwritten formula sheets to the exam. Difficult-to-remember formulas are supplied at the exam sheet (see old exams).

- You may use a simple pocket calculator, but no smartphones (only arithmetic assistance is allowed.)

- Slides that are not required for the exam are preceded by an "OPTIONAL SLIDES" header.

In [1]:

```
open("../../styles/aipstyle.html") do f
display("text/html", read(f,String))
end
```

In [ ]:

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```