### 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 - Broken Link
- 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)

### Discrete Math and Probability drps, drive Edit on GitHub

DMMR and PwA has been merged to form this new course, DMP is now solely based on coursework but you can find information about past papers here.

**Online (flipped-classroom) version of this course. 10x better than the lectures.**

Exam type: 100% Coursework.

**Discrete Mathematics:**

- Some inf1-cl links may be useful.
- Companion website for textbook
- List of topics per exam
- Trev tutor on DMMR and part 2 - similar to Khan academy
- Course notes by Edwin Onuonga (2017-18)
- Strategic Summary Notes by Maksymilian Mozolewski (2019-20)
**Videos**- Surjective and Injective functions
- Proof by Induction
- Strong Induction
- RSA Encryption Example - two parts: computing an example, generating the keys
- Introduction to Combinations, Introduction to Permutations
- Permutations: accounting for repetitions
- Relations and the different kinds of relations… and equivalence relations. This guy is great, isn’t he?

- Proofs:
- Worksheet: Proofs involving functions - with sample proof + proving injectivity/surjectivity
- by Induction: solutions to an MEI question (todo)

- 2016 worksheet page
- Congrugence modulo (Khan Academy)
~~Order of Complexity~~[Not examinable 2018]- A great tutorial on Baye’s theorem — read from
*Anatomy of a Test* - The order of mixed quantifiers - Is it equivalent if you swap ∀s and ∃s?

**Probablity**

- better tutorial/assignment list with some assignment solutions CORRECTED
- super handy expectations sheet
- Summary from the course textbook - The last four pages before the index in the Ninth Edition
- Slader Textbook Solutions - 9th Edition
- Cheat Sheet (source)
- Another cheat sheet with guides on distributions
- Joint probability distributions
- Conditional Probability Visualisation
- A visual introduction to probability and statistics (Seeing Theory)
- Coin Problem - 3 heads occurring before 2 tails (continuous toss)
- Course Summary
**2017-2018 notes by Edwin Onuonga****Videos**- Binomial probability (Khan Academy)
- Intro to Poisson distribution (jbstatistics)
- Intro to probability (followed by conditional probability) - playlist (Trefor Bazett) - this video to end of playlist
- Bayes Theorem (Michel van Biezen)
- Calculus: Iterated integrals (KristaKingMath)

**Calculators**

### 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**

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

### 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

### INF2C - Software Engineering Edit on GitHub

- No book is
*essential*for this course. - Exam,
**closed book**:- Pass requirement: 40% overall (exam+coursework combined), and 40% in the exam

**Detailed lecture notes**by Bora M. Alper: pdf, source- UML Basics: introduction, class diagram, sequence diagram
- Presentation about UML
- Software Requirements Document for a Curricular Information System
- Use Case Scenario/Diagram Example
- 2017’s coursework, “a tour guide app”:
- 2016’s coursework content, “a restaurant order-management system”: