Freddie Karlbom

Introduction to Recommender Systems Week 1

Basics of recommender system conceptually; learning from signals from other actors. In this sense, a movie review in a newspaper is a recommender system.

Can also be seen in a group of ants spreading out. Once one find a piece of food, they will lay a trail that will be followed by other ants and soon all will group to the piece of food.

Algorithms 2 - Week 3 - Dynamic Programming

Principles:

• Identify a small number of smaller subproblems
• Can quickly solve larger subproblems recursively given the solutions to the smaller problems
• After solving all subproblems, can compute the final solution

Algorithms 2 - Week 2 - Union-Find

More on greedy algorithms, then clustering, union-find data structure and Huffman Codes. I mostly wanted to speed through it and on to the next week where dynamic programming and other topics I’m more interested in starts, but ended up having to spend quite some time on the second part of the programming assignment.

Algorithms 2 - Week 1 - Greedy Algorithms

First week on the second part of the course.

Greedy Algorithms: Make decisions on incomplete information based on what looks like the best way forward at the time.

It is usually easy to find some alternative solutions for many problems and to do running time analysis. On the downside, it is oftentimes hard to establish correctness. In fact; greedy algorithms do oftentimes not find the optimal solution, but hopefully a solution that is good enough.

Algorithms 1 - Week 6 - Hash Tables & Bloom Filters

• Hash Tables
• Bloom Filters

Not that much of note, but having finished the quiz and programming assignment for this week, only the final exam remains to complete the course.

Nice refresher on data structures and algorithms, looking forward to part 2 which with greedy algorithms and dynamic programming should tie in nicely with another I’m currently studying up on, namely reinforcement learning.