### General guide, mailing list archives Edit on GitHub

**Honours hurdles**: refer to your DRPS programme- InfBase: a drop in helpdesk for you to get additional tutoring and support with your courses. See the schedule here - there’s no need to sign up, just drop in

### INF2D - Reasoning and Agents | drps, info, papers April/May exam Edit on GitHub

50% closed book exam, 30% across two courseworks, 20% tutorial / engagement. Pass: 40% overall

- MCQ Question Cache
- Quizlet
- Python implementation of algorithms from Russell And Norvig’s “Artificial Intelligence - A Modern Approach”
- Alpha-Beta pruning interactive example
- Another AB pruning example - allowing you to create your own tree
- Video on a-B pruning
- Good set of video lectures on most things covered in the course
- Breadth first search & depth first search
- PDDL Reference
- Bora M. Alper’s PDDL Companion - syntax check and plan PDDL from your terminal
- Bora M. Alper’s Lecture Notes (2019)

### INF2-FDS - Foundations of Data Science | drps, info Edit on GitHub

(New course: Details are sparse. Please contribute!)

50% coursework, 50% exam.

**General Reading***Artificial Intelligence: A Modern Approach*by Stuart Russell and Peter Norvig*Computational and Inferential Thinking*by Ani Adhikari and John DeNero*An Introduction to Data Ethics*by Shannon Vallor et al.*Modern Mathematical Statistics with Applications*by Devore & Berk

**Basic Tutorials****Cheatsheets****Course Notes**- NotAllThingsFDS_w1w9 (2021-2022, Semester 1)

### INF2-IADS - Intro to Algorithms and Data Structures | drps, info, papers April/May exam Edit on GitHub

- Introduction to ADS (gitbook, non inf)
- Amazing interactive examples from from USFCA
- Wikibook covering most of the stuff we are doing
~~Karatsuba Multiplication in 13 minutes (video, watchable at 1.25x)~~- Unofficial Past Paper Solutions
- Course Notes INF2B - Algorithms (2018)
- Community Solutions to CLRS (4th ed.)
- For all things iads: Abdul Bari

### INF2B - Learning Edit on GitHub

75% closed book exam, 25% across two courseworks. Pass: 40% overall.

- What is a neural network? (3blue1brown)
- Using neural nets to recognise handwritten digits
- Revision Formulae for Learning Thread (pdf) (LaTex)
- Learning notes (2017-18) by Edwin Onuonga (html, pdf)
*Missing*: perceptrons, single-layer and double-layer neural networks sections **Pearson Correlation Coefficient**

**coursework**

- MATLAB for use at home (free)
- Installing GNU Octave on macOS (much lighter than MATLAB)
**NumPy**: quickstart tutorial- Example Lab using numpy, scipy, pandas, and matplotlib: Similarity and recommender systems
**Why can’t I paste using Ctrl+V in MatLab???**- The default settings are odd. Go to Preferences -> MATLAB -> Keyboard -> Shortcuts, change*Emacs Default Set*to*Windows Default Set*.**Run MATLAB scripts from the command line**- Inf2B File Checker
- Intuition of the relation between PCA and eigenvectors (useful for 2019 coursework)
- Relevant bits from Vision processing at Stanford:
- Interactive veronoi knn explorer
- Interactive SVM examples, similar to discriminant functions
- Linear classifiers the content seems to better explain the lectures on NN and discriminant functions