# Course Outline and Administrative Issues¶

### Course comes in Two Parts¶

##### Part II: Model Complexity Control and the MDL Principle¶
• $4 \times 4=16$ lecture hours, mostly in March
• Instructor: Tjalling Tjalkens, rm. FLUX-7.101
• email [email protected]

### Why Take This Class?¶

• Suppose you need to develop an algorithm for a complex DSP task. This is what you'll do:

1. 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.
2. 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:
1. model selection, i.e., find $k^*$ (mostly discussed in part 2 of this class)
2. parameter estimation, i.e., find $\theta^*$ (mostly in part 1 of this class)

### Materials¶

• Contains much more material; great for future study and reference.

### Exam Guide¶

• 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).
• Slides that are not required for the exam are indicated by the word (OPTIONAL) in the header.
• Very strong advice: Make old exams!
• 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.)
• Further exam instructions for part-2 from Tjalling.
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