| Date | Topic | Resources | Evaluation | Feedback |
|---|---|---|---|---|
| 18-07-2026 | TinyCStr description; Sly Lexer/Parser demo |
SLY (Sly Lex Yacc) TinyCStr description Sly Lex Yacc Calculator Demo |
||
| 21-07-2026 | Lexer of TinyCStr Level1 | Partial Implementation with TODOs Level 1 Lexer |
python example.py' is executed,
the Python interpreter first compiles example.py
into bytecode (.pyc), which is then
executed by the Python Virtual Machine (PVM).
Like Java, Python uses bytecode and a virtual machine.
The difference is that the JVM uses Just-In-Time (JIT)
compilation for faster execution, whereas the PVM executes the
bytecode directly." | Date | Topic | Handout |
|---|---|---|
| 01-04-2026 | Qiskit Basics, Classic Vs Quantum Bits | Click here |
| 08-04-2026 | Single-Qubit Gates & Bloch Sphere Visualisation | Click here |
| 13-04-2026 | Multi-Qubit Gates & Entanglement | Click here |
| 17-04-2026 | Bell State Preparation & Verification | Clickhere |
| 17-06-2026 | Deutsch-Jozsa Algorithm | Clickhere |
| 15-07-2026 | Grover’s Search Algorithm | Clickhere |
Classical computers are built from semiconductor chips containing billions of transistors. For decades, the number of transistors on a chip has roughly doubled every two years, a trend known as Moore’s Law. However, as transistors approach atomic scales, continuing this trend is becoming increasingly difficult, motivating the exploration of new computing paradigms such as quantum computing.
| Date | Topic | Slides | Lecture Notes |
|---|---|---|---|
| 09-03-26 | Introduction, Why Quantum
Computing? Milestones in quantum theory (Planck, Einstein, Bohr) |
Slides | Notes |
| 30-03-26 | Linear Algebra, Hilbert Spaces and Quantum Mechanics Foundations |
Slides | Notes |
| Read | Role of Biology in Quantum Computing | Slides | Notes |
| 15-04-26 | Classical vs. Quantum Logical Operations | Slides | Notes |
| 22-04-26 | Classical Computing Limitations | Slides | Notes |
| Date | Theory Topic | Practical topic | Resources | Submission |
|---|---|---|---|---|
| 01/12/2025 | Introduction to Machine Learning | Numpy and Pandas | Theory , Lab , Data Set | Submit |
| 08/12/2025 | Data Pre-Processing and Feature Engineering | Data Cleaning, Transformation, Feature Engineering with Numpy, Pandas | Theory , Lab | Submit |
| 15/12/2025 | Data Visualization with Matplotlib & Seaborn | Lab | Submit | |
| 22/12/2025 | Recap | |||
| 29/12/2025 | K-NN and Naive Bayes | K-NN and Naive Bayes using scikit-learn | Theory, Lab | |
| 05/01/2026 | Decision Tree Classifier | Decision Tree Classifier using scikit-learn | Theory, Lab | |
| 12/01/2026 | Support Vector Machine(SVM) based classifier | Support Vector Machine(SVM) based classifier using scikit-learn | Theory, Lab | |
| 16/02/2026 | Linear Regression using scikit-learn | Theory, Lab |
