Semester: August - December, 2024
The course information document is here: Course Information
Parvathy N Sujith C Diwakar BR Neelakshi G
- Introduction
Lecture Slides - Vectors
Lecture Slides | Video lectures: [1] [2] [3] | Tutorials: [1] [2] - Matrices
Lecture Slides | Video lectures: [1] [2] - Solution to Linear Equation
Lecture Slides | Video lectures: [1] [2] - Case Study 01 + Applications
Lecture Slides | Video lectures: [1] [2] | Notebooks: [01] [01b] - Orthogonality
Lecture Slides | Video lectures: [1] [2] [3] - Matrix Inverses
Lecture Slides | Video lectures: [1] [2] - Application: Signal Processing
Lecture Slides - Eigenvalues and Eigenvectors
Lecture Slides - Positive Definiteness and Matrix Norms
Lecture Slides - Application: Linear Dynamical Systems
Lecture Slides - Singular Value Decomposition
Lecture Slides - Application: Dimensionality Reduction & PCA
Lecture Slides - Unconstrained Optimization
Lecture Slides - Constrained Optimization
Lecture Slides - Additional Topics in Optimization
- Linear Least Squares & its Variants
Lecture Slides - Application: Signal/Image Processing
- Application: Machine Learning
- Linear Programming & Simplex Method
- Application: Neuromechanics, Radiotherapy
- Introduction to Probability
- Introduction to Statistics
- Outro
The notes for the course can be found here: ALADA Notes
A one-page summary of the course can be found here. This document is still a work in progress. We will update this as we progress through the course. You are encouraged to keep a copy of this document with you on your laptop or your phone for quick reference during class.
All assignment-related information is available in the assignments folder of the repository. The assignment problems can be found in the individual chapters of the course notes. All assignments will be due by 11:59 PM on the due date. You have 3 late days to use throughout the semester. Each late day extends the deadline by 24 hours. After the late days are used, late submissions will not be accepted.
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Assignment 01 [20 Oct 2024]
1.1 Vectors | Problems: Ch: 1, Ex: All problems | Data: expt1.csv
1.2. Matrices | Problems: Ch: 2, Ex: All problems -
Assignment 02 [31 Nov 2024]
2.1. Solution to Linear: Ch:3, Ex: All problems
2.2. Case Study 01 | Problems: [CS01] [CS01b] | Data: tokens-010.txt, tokens-025.txt
2.3. Orthogonality: Ch: 4, Ex: All problems -
Assignment 03 [10 Nov 2024]
3.1. Matrix Inverses
3.2. Signal Processing -
Assignment 04 [20 Nov 2024]
4.1. Eigenvectors and Eigenvalues
4.2. Linear Dynamical Systems -
Assignment 05 [30 Nov 2024]
5.1. Positive Definite Matrices
5.2. SVD
5.3. Dimensionality Reduction -
Assignment 06 [10 Dec 2024] \ 6.1. Linear Least Squares
We will have regular tutorial sessions to solve problems in class on topics covered until that point. The tutorials file is available here
All assignments must be submitted as soft copies on the submission portal.
Mid-Semester Exam: 15 October 2024, 7:30 - 8:30 AM
Final Exam: 21 December 2024, 7:30 - 10:30 AM
The repository also has a set of interactive animations to demonstrate some of the concepts covered in the course. You can find details about these animations here.