Teaching

Course materials and resources for classes taught. This page is based on the structure of the "Intro to ML" course and contains a draft syllabus, schedule, and placeholders for slides, assignments and code.

This page collects course materials, lecture notes, assignments, and links for courses I’ve taught. The content below is a draft based on the “Intro to ML” course structure; some materials (slides, datasets, GitHub) are left as placeholders where the original site requires access.

Course: Intro to Machine Learning (drafted from IITGN “Intro to ML” site) Course: Intro to Machine Learning (IITGN short course — exact details below)

Course summary: An introductory short course that builds concepts from fundamentals and equips students with intuition and mathematics behind ML algorithms. Students will implement classical ML techniques and gain hands-on experience.

Pre-requisites:

  • Basic Linear Algebra (MA103 level is sufficient)
  • Basic Probability
  • Familiarity with Python, NumPy, and pandas. Knowledge of PyTorch is preferable but not required.

Learning objectives:

  • Learn mathematical foundations (SVD, matrix calculus, probability distributions) used in ML
  • Implement and understand unsupervised methods (clustering, dimensionality reduction)
  • Implement and understand supervised methods (linear/logistic regression, decision trees, ensembles)
  • Understand neural network basics including MLPs and convolutional neural networks
  • Apply regularization, optimization, and model-selection techniques (cross-validation, hyperparameter tuning)

Day-wise lecture schedule (as in the course PDF):

  • 31/10/2025 — Mathematical Foundations for ML
    • SVD
    • Matrix calculus
    • Probability distributions
    • Time: 21:30–22:50 — Instructor: Ridham Patel
  • 04/11/2025 — Unsupervised Learning (Clustering)
    • K-means, hierarchical clustering
    • Dimensionality reduction; curse of dimensionality
    • Parametric vs non-parametric methods
    • Matrix factorisation (if time permits)
    • Time: 21:30–22:50 — Instructor: Ridham Patel
  • 06/11/2025 — Decision Trees and Ensembles
    • Tree construction: splitting, entropy, information gain
    • Overfitting/underfitting in trees
    • Random forests intuition; feature importance; hands-on tree algorithms
    • Time: 21:30–22:50 — Instructor: Aryan Solanki
  • 10/11/2025 — Linear Regression & Mathematical Intuition
    • Simple linear regression: fitting and prediction
    • Geometric perspective, cost functions
    • Variants (e.g., polynomial regression)
    • Logistic regression and logits; bias–variance tradeoff
    • Time: 21:30–22:50 — Instructor: Ridham Patel
  • 11/11/2025 — MLPs & Gradient Descent
    • Perceptron, Multi-layer Perceptron (MLP)
    • Activation functions: ReLU, Sigmoid, Tanh
    • Probability interpretation; gradient descent and backpropagation
    • Time: 21:30–22:50 — Instructor: Aryan Solanki
  • 13/11/2025 — Autograd and Regularisation
    • Autograd systems: computational graphs and automated chain rule
    • Intuition for regularization; Ridge and LASSO
    • Hyperparameter tuning and cross-validation (including k-fold)
    • Time: 21:30–22:50 — Instructor: Romit Mohane
  • 14/11/2025 — Convolutional Neural Networks
    • Convolution operation: intuition and mathematics
    • Convolutional layers, pooling, strides, padding
    • Modern architectures overview (LeNet, ResNet concepts)
    • Time: 21:30–22:50 — Instructor: Romit Mohane

General instructions (from PDF):

  • Additional reading material will be provided for interested readers with every lecture.
  • Code will be provided with every lecture/topic where applicable; students are encouraged to implement the code themselves to gain hands-on experience.

Registration: Closing date/time in the PDF — 30th October 2025, 17:00.

Course materials:

  • Lecture slides, code, and additional readings will be linked here when available. The original short-course PDF is available as an attachment.

Attendance: This short course was attended by 200+ students — the largest short course ever held at IITGN.

If you want, I can:

  • Add a downloadable copy of the PDF into the site’s assets and link it here.
  • Convert this single page into a _teaching/ collection with one markdown per lecture (useful for per-lecture notes, slides, and code).
  • Add direct links to slides/code if you provide the URLs or files.