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Verticals
Computer Vision
Computer vision is a field of artificial intelligence (AI) that enables robots to derive meaningful information from digital images, videos, and other visual inputs — and take actions or make recommendations based on that information. Computer vision trains machines to perform various functions through cameras, data, and algorithms. From an engineer's perspective, it seeks to understand and automate tasks that the human visual system can do.
Schedule
Syllabus
Installing of required Python libraries, Virtual OS/Dual Boot, Sorting difficulties faced by students.
Syllabus
Basics of Python, Numpy, Introduction to OpenCV, Digital Images, Basic Functions of OpenCV, Color Scale Transformations, Color Extractions
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Image Arithmetic, Masking, Geometric Transformations, Morphological Operations, Doubts & Discussion 1.
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Kernel Operations(Blurring Techniques, Edge Detection Techniques, etc.), Data Structures, BFS, DFS, Blob Detection.
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Contours, Template Matching, Corner Detection, Hough Transforms, Video Capturing and Video Functions, Doubts & Discussion 2.
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Trees, Graphs, Path Planning algorithms(BFS,DFS, Djikstra,A*,RRT).
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Basics of Arduino, Connecting Arduino with Python Code, Problem Statements Discussion, Doubts & Discussion 3.
Autonomous Robotics
Autonomous Robotics designs and engineers robots to deal with their environment on their own, and work for extended periods of time without human intervention. They often have sophisticated features that can help them to perceive their environment, make decisions based on what it perceives and/or has been programmed to recognize conditions and then actuate a movement or manipulation within that environment. An autonomous robot is a robot that acts without recourse to human control.
Schedule
Syllabus
Installation of Ubuntu, basics of Github.
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Basic electronics, H-Bridge, ICs, PWM sequential/combinational circuits, sensors(overview)
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Basic AVR [GPIO, Timers, PWM] (basic introduction),PWM [Implementation, ADC, Interrupts] (overview).
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Microcontroller architecture, AVR C, bitwise operators, basic C & Arduino programming bot demonstration, introduction to PS.
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Control loops, feedback mechanism, PID implementation,ROS (Environment setup, general ROS cmdline, nodes, topics, msg files, rqt service and launch).
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ROS (Publisher, subscriber, bag file), ROS Serial, RViz and Gazebo Introduction, Doubt clearance.
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Problem statement implementation and debugging.
Machine Learning
Machine learning is a branch of artificial intelligence that focuses on using data and algorithms to imitate how humans learn, gradually improving accuracy. It involves the use and development of computer systems that learn and adapt without explicit instructions. It employs algorithms and statistical models to analyze and draw inferences from patterns in data. Hence it allows the computers to learn automatically without human intervention.
Schedule
Syllabus
To be updated soon.
Syllabus
Setup env, Intro to python, Intro to visualization techniques (matplotlib, seaborn), Intro to Numpy, Pandas, Cost function & Gradient Descent, Data Preprocessing, Linear and Logistic Regression, Implementation using Scikit-learn, Overfitting and Underfitting, Evaluation Metrics.
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Support Vector Machines, KNN, Random Forest, Decision Trees, Boosting (XGBoost, CatBoost, AdaBoost), Bias-Variance Tradeoff, Hyperparameter Tuning, Code Implementation, Project - 1.
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What is Deep Learning?, CNN, Overview of various classic CNN models, Object detection with YOLO Algorithm, Intro to Semantic Segmentation with examples.
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What is Natural Language Processing?, RNN, LSTM, Intro to PyTorch workflow, Basic Implementation of CNN and RNN using PyTorch.
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Clustering Techniques: K-means, Hierarchical Clustering, Bayesian Classifier, Code Implementation.
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Intro to Time Series Methods, Deep Learning for Time Series Forecasting, Latent space methods, Principal Component Analysis, Project 2 and 3.