# Class notes and slides

I post the class slides here, they appear usually after the class, I encourage you not to depend on the slides but to take old fashioned notes, because the material will be easier to learn that way. Class notes are an amalgamation of my own slides and slides made by Janet Peterson and John Burkart.

## Contents

### Introduction

Syllabus and class overview (Lecture in PDF format)

### Algorithms and Design

Week 1: Week 1 Lecture in PDF format; Week 2:Week 2 Lecture in PDF format ( old lecture: Lecture in PDF format)

The slides discuss algorithm efficiency, **brute force** algorithms for sorting and searching, a **transform and conquer** algorithm for searching, **decrease and conquer** algorithms for sorting and finding a fake coint; a **greedy algorithm** for coin changeing, **divide and conquer** algorithms for sorting, searching, large integer multiplication and matrix multiplication. These lecture notes were originally prepared by Janet Peterson and slightly edited by Peter Beerli.

### Random Processes

Lecture in PDF format

The slides introduce probability, calculation of probability for combined events, and short exposition of random numbers (historically and with matlab). Introduction of probability density function, cumulative density functions, examples with dice (regular and cheated ones).

### Graphs

Lecture in PDF format

The slides introduce graphs and their representation in software, exploration of finding one or all path on a graph, finding the number of components, finding the shortest path between two points, traveling salesman problem etc.

### Data mining: Clustering, Decision trees

Lecture in PDF format

The slides introduce clustering methods and how to use and construct decision trees, and towards the end give glimpse into machine learning.

### Discrete Optimization

Lecture in PDF format

Discrete optimization solves problems as supply and stocking problems, optimal knapsack filling, traveling salesman problems, sequence alignment, among others using linear programming or dynamic programming, etc.

### Image manipulation

Lecture in PDF format

The slides explain the manipulation of images, color reductions, removal of noise, sharpening, and lighten pictures, among others. Here is the link to a folder provided by John Burkardt that contains the images and some of the matlab files mentioned in the slides: image directory