Mathematics for Data Science 2 (2020)

Welcome to MATH7502. This course is part of the Masters of Data Science Program at The University of Queensland.
The course is coordinated and lectured by Yoni Nazarathy (y.nazarathy@uq.edu.au). The tutors are Samuel Hambleton (samuelahambleton@gmail.com) and Wala Draidi (wala.draidi@hotmail.com).

Outside of the Zoom lectures and practicals, communication dealing with course content matters should be via Piazza or via the Zoom consultation hours. Formal messages and grades are broadcasted via blackboard.

You may want to look at last year's course. A good part of the material is similar but the structure is different. You may also want to refer to the official course profile for grading information.

Outside of the Zoom lectures and practicals, communication dealing with course content matters should be via Piazza or via the Zoom consultation hours. Formal messages and grades are broadcasted via blackboard.

You may want to look at last year's course. A good part of the material is similar but the structure is different. You may also want to refer to the official course profile for grading information.

This course has a heavy focus on some of the mathematics used in data science applications. It is a linear algebra course that also incorporates some multi-variable calculus. The foundations of linear algebra are studied and explored with the help of numerical examples from the Julia programming language. The assignments and final project also celebrate these 12 data science use cases:
**Background:** The prerequisite for the course is knowledge comparable to MATH7501. This includes basic discrete mathematics, calculus and elementary manipulation of vectors and matrices. Feel free to use the 7501 - course reader to brush-up as needed. If you haven't done MATH7501, you may also use the mathematics first year learning centre to get help on elementary (MATH7501'ish) items such as basic matrix/vector operations, basic calculus, basic understanding of mathematical notation. It is also recommended that you read the following sections from the [VMLS] book as background: 1.1, 1.2, 1.3, 1.4, 3.1, 3.2, 6.1, 6.2, 6.3, 6.4, 7.1, 7.2, 7.3, 10.1, 10.2, 10.3.

- Clustering
- Convergence proof for the perceptron
- Least squares data fitting
- Least squares classification
- Multi-objective least squares and regularization
- Multiple ways for evaluating least squares
- Linear dynamical systems and systems of differential equations
- Covariance matrices and joint probabilities
- Multi-variate Gaussian distributions and weighted least squares
- Cholesky decomposition for multi-variate random variable simulation
- Analysis of gradient descent and extensions
- Principal component analysis (PCA)

The course makes use of the following resources and materials:

**[CLAWJ]**Core Linear Algebra with Julia. These are core linear algebra materials mostly available as Jupyter notebooks. See here.**[DSUC]**Data Science Use Cases. Material useful for the 12 uses-cases mentioned above. See here.**[JULIA]**The Julia programming language. See information here.-
**[VMLS]**The book: Vectors Matrices and Least Squares (2018) by Stephen Boyd and Lieven Vandenberghe. You can use the free on-line version or you can order the book. Here is the Julia Language Companion for the book. -
**[LALFD]**The book: Linear Algebra and Learning from Data (2018) by Gilbert Strang. Students may wish to obtain a copy: university book store. There are (2 x 24 hour loan) copies at UQ Library. The UQ Library has also scanned critical sections from the book here. -
**[ILA]**The book: Introduction to Linear Algebra, Fifth Edition (2016) by Gilbert Strang. Here it is in the university book store. Selected sections are in the UQ Library. -
**[3B1B]**The video series: Essence of linear algebra by 3Blue1Brown (Grant Sanderson). -
**[SWJ]**The draft book: Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence (2020) by Yoni Nazarathy and Hayden Klok. Code examples from the book are available in this GitHub repo.

The course assessment includes two assignments, three quizzes, and a final project:
**Quiz, assignment, and project submission information**: All assessment items are to be submitted to mathdatasciencebridgingsubmissions@uq.edu.au (this account should not be used for any queries - only for submissions). Submissions should include two files, a PDF file and an audio clip. The submission must adhere to the following guidelines:

- Submit a single PDF file, with pages of uniform size, and a file size that does not exceed 8MB (you can use a pdf compression utility if needed). The name of the PDF file should be FFFF_LLLL_SN-IIII.pdf where FFFF is your first name, LLLL, is your last name, SN is your student number, and IIII is "Quiz1", "Assignment1", "Project", etc.
- Do not submit code - instead format your code into the PDF file.
- Both handwritten notes and typed notes are acceptable. However typed (LaTeX/Jupyter) mathematics is preferable.
- All graphs, plots, source code, and other figures must be clearly labeled.
- All questions/items must appear in order.
- A recorded audio clip in any standard format with a minimum duration of one minute and a maximum duration of two minutes. The file size may note exceed 4MB. In your recording, state your name, and your experience with this assignment. Mention the resources that you used to carry out the assignment, and if valid, indicate that you did not plagiarize. Name the audio clip in the same way that your PDF file is named, but with the valid audio format extension.

Lecture and practical recordings are here.

Other material from the lectures and practicals is in this GitHub repo.

Here is the schedule:

Project due: Nov-14.

Other material from the lectures and practicals is in this GitHub repo.

Here is the schedule:

Week | Monday Date | Lecture | Thursday Date | Practical | Assignment Due |
---|---|---|---|---|---|

1 | Aug-3 | 2.5 hrs | Aug-6 | Julia intro | - |

2 | Aug-10 | 2.5 hrs | Aug-13 | Julia linear algebra | - |

3 | Aug-17 | 2.5 hrs | Aug-20 | Ass 1 guidance | - |

4 | Aug-24 | Quiz 1 + 1 hr | Aug-27 | Quiz 1 Sol | - |

5 | Aug-31 | 2.5 hrs | Sep-3 | Ass 1 guidance | - |

6 | Sep-7 | 2.5 hrs | Sep-10 | Quiz 2 prepare | Ass 1 Due: Sep-10 |

7 | Sep-14 | Quiz 2 + 1 hr | Sep-17 | Quiz 2 Sol | - |

8 | Sep-21 | 2.5 hrs | Sep-24 | Ass 2 guidance | - |

Break | - | - | - | - | - |

9 | Public Holiday (Oct-5) |
- | Oct-8 | Ass 2 guidance | - |

10 | Oct-12 | 2.5 hrs | Oct-15 | Quiz 3 prepare | Ass 2 Due: Oct-15 |

11 | Oct-19 | Quiz 3 + 1 hr | Oct-22 | Quiz 3 sol | - |

12 | Oct-26 | 2.5 hrs | Oct-29 | Project guidance | - |

Project due: Nov-14.