Notes: Classical ML
These notes mirror our 2-month program on classical machine learning (AlphaML), where we start from the basics (of python & math), and go to building advanced models, understanding practical real-world considerations, and showcasing all of it to the world!
Curriculum
- Intro to Programming Languages
- Implementation: Variables, Assignment Statement, Comments, Data Types, If-else, Indentation
- Implementation: Typecasting and Looping
- Implementation: "Find and Replace", Strings, Functions, Modules, and Packages
- Implementation: Data Structures - Lists and Strings
- Implementation: Data Structures - Dict, Tuples, and Sets, (b) Operators and ShortHand Notation
- Implementation: Classes, Objects, and Inheritance
- Library: Pandas
- Library: Matplotlib
- Basics of Exploratory Data Analysis (EDA)
- Library: Sklearn
- Introduction to Vectors, Scalars, Norm, Basis, and Linear Independence
- Matrix as a Linear Transformation, Operations on Matrices
- Solving Ax=b and its geometric interpretation
- Gaussian Elimination
- Norms, Special Matrices, Vector Algebra
- Determinants
- Eigen Decomposition, SVD, Trace, Pseudoinverse
- Generalised Covariance Matrix with Geometric Meaning
- Intro to Probability
- Basic Calculus Formulae
- Constrained Optimization
- Supplementary Resources
- Theory + Implementation: Regularisation (Ridge, Lasso, ElasticNet)
- Bias and Variance of a Model, Generalisation
- Statistical Measures, Feature Selection, and Dimensionality Reduction
- Implemention: Feature Selection and Dimensionality Reduction
- Implementation: Handling Missing Values + Categorical Values with Pandas
- Theory: More Methods on Dealing with Missing Values
Instructor: Abbhinav Venkat
With a rich background that bridges both academia and the dynamic world of Silicon Valley startups, I've built our programs to encapsulate just what is required for you to excel in Machine Learning!
My tenure at prestigious institutions like Stanford and IIIT-H has provided me with a deep theoretical understanding, while my journey from the ground up in Silicon Valley startups has granted me practical, hands-on experience in building real-world machine learning systems.
As someone who has not only contributed to the field through publications in top-tier conferences like ICCV, BMVC, and ACCV but has also been felicitated by the Govt. of India and Qualcomm's Innovation Fellowship, I am zealous about sharing insights and fostering a passion for machine learning among my students.