### Exam

Equipment

Only pens and simple calculators (nelilaskin) or function calculators (funktiolaskin) allowed. Books, notes, phones, computers, graphical calculators etc. are __not__ permitted.

#### Grading

Maximum number of points is 50. The minimum required for passing the course is 25. Grading emphasizes conceptual understanding over mathematical precision. Please note that equivocated answers -- i.e., fishing points by generating answers that are blatantly false or might in real life have damaging consequences -- will be penalized by deducing points.

#### Contents and learning objectives

**Primary material**: 1) Lecture slides and 2) assignments marked as Recommended. These are sufficient for a passing-to-good grade. Pay attention to topics marked as "Learning Objectives". **Secondary materials**: To aim for the highest grade, we advise reading selected papers listed in Materials as well as papers flagged in the assignments.

#### Format and task types

The exam will consist of 10 pages. Each page will contain one task worth of max. 5 points. The following task types may be used to test **general understanding**:

- Definition: E.g., define a concept in text or by a diagram.
- Explanation: E.g., explain a concept, model, or theory briefly in text or by a diagram.
- Assessment of a theory or model: E.g,. analyze pros and cons of a given theory, model, or concept.
- Short essay: E.g., provide an account of some phenomenon in interaction from a perspective coming from the course materials.

The following task types may be used to test the ability to apply knowledge to **practical problems**. In these problems:

- Analysis: E.g., given a design, analyze its different aspects from the perspective of a concept, model, or theory.
- Comparison: E.g., given two designs, analyze their pros and cons from the perspective of a concept, model, or theory.
- Numerical problem: E.g., given a design, identify the value of some property or outcome using a model.
- Re-design: E.g., given a design, propose a simple improvement by reference to a concept, theory, or model.
- Assessment of a design: E.g., given a design, analyze its pros and cons using appropriate models, concepts, or theories provided in the course. Assessment can be verbal or numerical.

**Checklist**

LECTURE 1: INTRODUCTION

(1) Concepts: Objective function; Constraint; Task instance; Optimization; Predictive modelling; Inference; Sensitivity analysis. (2) Computational interaction: characteristics as a research topic and examplary applications. (3) Example task: Define "element grouping" as an objective function.

LECTURE 2: INTEGER PROGRAMMING

(1) Integer programming: what and why and why not; (2) Concepts: Integer program; decision variable; objective function; constraint. (3) Example application: The same as above for Lecture 1 but as an integer program.

LECTURE 3: BLACK BOX OPTIMIZATION

(1) Black box optimization as opposed to exact methods. (2) Concepts: heuristic optimization; meta-heuristic optimization, be able to explain e.g. how simulated annealing works. (3) Example task: shown a piece of code implementing a heuristic approach for computational UI generation, tell about pros and cons of the approach.

LECTURE 4: HUMAN PERFORMANCE MODELS

(1) Concepts: Response processes. (2) Models: Fitts' law (a must!), Hick's law, KLM (on slides). (3) Example task: Analyze a UI using a model and propose an improvement.

LECTURE 5: PROBABILISTIC DECODING

(1) Probabilistic decoding, its relationship to information theory (limited capacity channel), and example applications; (2) Define a probabilistic decoder; (3) Example task: Explain how Bayes theorem is used in a touch or language model.

LECTURE 6: BAYESIAN OPTIMIZATION

(1) What, why, and when, difference to combinatorial optimization; example applications. (2) Concepts: proxy model, acquisition function, exploration/exploitation problem. (3) Example task: Given a concrete task (think: the color preference problem or similar), explain how BO works.

LECTURE 7: BANDITS

(1) What and when and why; applications; also: difference to BO; (2) Concepts: the bandit problem, multi-armed bandit, contextual bandit, regret. (3) Example task: e.g., explain how bandits can be used to recommend designs to a designer. (4) Thompson sampling: if you get this, great!

LECTURE 8: HUMAN VISUAL SYSTEM MODELS: SALIENCY

(1) Visual saliency: definition (important!) and applications; (2) Be ready to explain: How "classical" (non-parametric) models work (like Itti-Koch) versus newer, data-driven models? Pick one model from each category and be ready to explain; (3) Example task: given a graphical layout, explain what would draw attention according to a saliency model and why.

LECTURE 9: SIGNAL PROCESSING

(1) Input sensing flow, using mouse as the example; (2) Concepts: transfer function, information, sensor, recognition, hysteresis, calibration. (3) Example task: Filtering: learn at least one filter and be ready to explain (visually) how it works in input processing. Another: compute information in a simple input sensing scenario.