Doubt-Clearing Session

C/C++ Programming

1. Basics of C/C++ Syntax and Data Types
2. Control Structures: Loops and Conditional Statements
3. Functions and Modular Programming
4. Arrays, Pointers, and Memory Management
5. Structures and File Handling
6. Introduction to Object-Oriented Programming (OOP) in C++
7. Classes, Objects, Inheritance, and Polymorphism
8. Templates and Standard Template Library (STL)
9. Memory Management and Pointers
10. Advanced Topics: Concurrency, Data Structures, and Algorithms

Python Programming

1. Basics of Python syntax, data types, variables, and operators.
2. Control structures such as loops and conditional statements.
3. Functions and modular programming.
4. Data structures in Python: lists, tuples, dictionaries, sets, etc.
5. File handling and input/output operations.
6. Exception handling and debugging techniques.
7. Object-oriented programming (OOP) concepts: classes, objects, inheritance, polymorphism, encapsulation.
8. Advanced topics like decorators, generators, context managers, etc.

Data Structures

1. Fundamental data structures: arrays, linked lists, stacks, queues.
2. Trees: binary trees, binary search trees, efficient binary trees, multi-way search heaps.
3. Graphs: representation, traversal algorithms (DFS, BFS), shortest path algorithms (Dijkstra's, Bellman-Ford), minimum spanning tree (Prim's, Kruskal's).
4. Hashing and hash tables.
5. Advanced data structures: tries, segment trees, Fenwick trees, etc.

Design and Analysis of Algorithms

1. Basics of algorithm analysis: time complexity, space complexity, Big O notation.
2. Sorting algorithms: bubble sort, selection sort, insertion sort, merge sort, quick sort, radix sort, etc.
3. Searching algorithms: linear search, binary search.
4. String matching algorithms
5. Divide and conquer algorithms.
6. Greedy algorithms and techniques.
7. Dynamic programming.
8. Graph algorithms: traversal, shortest paths, minimum spanning trees, etc.
9. Computational complexity: NP-hard problems, approximation algorithms.

Image Processing and Computer Vision

1. Introduction to image processing: digital images, pixel representation, color models.
2. Image enhancement techniques: filtering, histogram equalization, noise reduction.
3. Image segmentation and feature extraction.
4. Object detection and recognition.
5. Basic principles of computer vision algorithms: edge detection, corner detection, object tracking.
6. Introduction to popular libraries and frameworks for image processing and computer vision in Python such as OpenCV, Pillow, scikit-image, etc.