{"id":6377,"date":"2024-04-07T23:00:00","date_gmt":"2024-04-07T18:00:00","guid":{"rendered":"https:\/\/corvit.com\/systems\/?page_id=6377"},"modified":"2024-07-12T16:51:34","modified_gmt":"2024-07-12T11:51:34","slug":"ai-robotics","status":"publish","type":"page","link":"https:\/\/corvit.com\/systems\/navttc-courses-outlines\/ai-robotics\/","title":{"rendered":"AI (Robotics)"},"content":{"rendered":"[vc_row type=”full_width_background” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” bg_color=”#cc3036″ scene_position=”center” top_padding=”2%” bottom_padding=”2%” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][vc_custom_heading text=”AI (Robotics) Course Outline” font_container=”tag:h1|font_size:40|text_align:left|color:%23ffffff|line_height:1.2″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:700%20bold%20regular%3A700%3Anormal” css=”.vc_custom_1714982142278{margin-bottom: 3% !important;padding-top: 4% !important;}”][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” top_padding=”28″ bottom_padding=”28″ text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none” shape_type=””][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][vc_column_text]Course Contents \/ Lesson Plan <\/strong><\/p>\n

Course Title: <\/strong>AI (Robotics)<\/p>\n

Duration: <\/strong>3 Months<\/p>\n

 <\/p>\n

MODULES<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Schedu<\/strong><\/p>\n

led <\/strong>Weeks<\/strong><\/td>\n

Module Title<\/strong><\/td>\nDays<\/strong><\/td>\nHours<\/strong><\/td>\nLearning Units<\/strong><\/td>\nHome <\/strong>Assignment<\/strong><\/td>\n<\/tr>\n
Week 1<\/strong><\/td>\nIntroduction to AI and Robotics<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nCourse Introduction and Expectations<\/td>\n\u00a0<\/strong><\/p>\n

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Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nIntro to AI and Robotics<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nJob Market Overview<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nWork Ethics in Institute<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nHistory of AI and Robotics<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nCurrent State of AI and Robotics<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nApplications of AI and Robotics<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nEthical Considerations in AI and Robotics<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Programming Languages used in AI and Robotics<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nVariables, Data Types, and Operators<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nControl Structures and Functions<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with a Programming Language<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Machine Learning (ML)<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nSupervised vs. Unsupervised Learning<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour 3<\/strong><\/td>\nLinear Regression<\/td>\n<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with a ML Algorithm<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Computer Vision<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nImage Processing Techniques<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nFeature Extraction<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with a Computer Vision Algorithm<\/td>\n<\/tr>\n
Week 2<\/strong><\/td>\nProgramming Fundamentals<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nSuccess Stories of AI and Robotics<\/td>\n\u00a0<\/strong><\/p>\n

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Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nRecap of Programming Concepts<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nIntroduction to Object- Oriented Programming (OOP)<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with OOP<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nData Structures and Algorithms<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nRecursion<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nBig O Notation<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with Data Structures and Algorithms<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Version Control Systems (VCS)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour 2<\/strong><\/td>\nGit Basics<\/td>\n<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nBranching and Merging<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with Git<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Web Development<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nHTML, CSS, and<\/p>\n

JavaScript<\/td>\n<\/tr>\n

Hour 3<\/strong><\/td>\nWeb Frameworks<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with Web Development<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Cloud Computing<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nCloud Providers<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nInfrastructure as Code<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with Cloud Computing<\/td>\n<\/tr>\n
Week 3<\/strong><\/td>\nMachine Learning Basics<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nMotivational Lecture on AI and Robotics<\/td>\n\u00a0<\/strong><\/p>\n

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Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nMultivariate Linear Regression<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nLogistic Regression<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with Regression Algorithms<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nSupport Vector Machines (SVM)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour 2<\/strong><\/td>\nKernel Tricks<\/td>\n<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nHands-on Practice with SVM<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with SVM<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nDecision Trees and Random Forests<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nEnsemble Methods<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nHands-on Practice with Decision Trees and Random Forests<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDecision Trees and Random Forests<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Neural Networks<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nPerceptron<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nMulti-Layer Perceptron (MLP)<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with Neural Networks<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Deep Learning<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nConvolutional Neural Networks (CNNs)<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nRecurrent Neural Networks (RNNs)<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with CNNs and RNNs<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Week 4<\/strong><\/td>\nComputer Vision<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nSuccess Stories of AI and Robotics<\/td>\n\u00a0<\/strong><\/p>\n

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Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nIntroduction to Image Processing<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nImage Filtering<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with Image Filtering<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nEdge Detection<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nFeature Extraction Techniques<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nHands-on Practice with Feature Extraction<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with Feature Extraction<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nObject Recognition<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nObject Tracking<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nHands-on Practice with Object Recognition and Tracking<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with Object Recognition and Tracking<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nSemantic Segmentation<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nInstance Segmentation<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nHands-on Practice with Segmentation<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour 4<\/strong><\/td>\nHands-on Practice with Segmentation<\/td>\n<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to 3D Computer Vision<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nStereo Vision<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nDepth Estimation<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on Practice with 3D Computer Vision<\/td>\n<\/tr>\n
Week 5<\/strong><\/td>\nNatural Language Processing<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Natural Language Processing (NLP)<\/td>\n\u00a0<\/strong><\/p>\n

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Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nText preprocessing and cleaning<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nTokenization and stemming<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nPart-of-speech tagging<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nNamed Entity Recognition (NER)<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nChunking and parsing<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nWord embeddings<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nLanguage modeling<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nSentiment analysis<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nTopic modeling<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour 3<\/strong><\/td>\nText classification<\/td>\n<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nInformation retrieval<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nMachine translation<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nDialogue systems<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nText summarization<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nNatural Language Generation (NLG)<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nEthical considerations in NLP<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nEmerging trends in NLP<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nPractical applications of NLP<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nHands-on NLP project<\/td>\n<\/tr>\n
Week 6<\/strong><\/td>\nReinforcement Learning<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Reinforcement Learning (RL)<\/td>\n\u00a0<\/strong><\/p>\n

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Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nMarkov Decision Processes (MDPs)<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nValue iteration and policy iteration<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nMonte Carlo methods<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nTemporal Difference (TD) learning<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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<\/td>\n<\/td>\n<\/td>\nHour 2<\/strong><\/td>\nSARSA algorithm<\/td>\n<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nQ-Learning<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDeep Q-Learning<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nExploration vs exploitation trade-off<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nMulti-armed bandits<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nUpper Confidence Bound (UCB) algorithm<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nThompson Sampling<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nPolicy Gradient Methods<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nREINFORCE algorithm<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nActor-Critic methods<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nAsynchronous Advantage Actor-Critic (A3C) algorithm<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nMulti-Agent RL<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nCooperative and competitive scenarios<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nMulti-Agent Deep RL<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nApplications of RL in robotics<\/td>\n<\/tr>\n
Week 7<\/strong><\/td>\nDeep Learning Basics<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Deep Learning (DL)<\/td>\n\u00b7\u00a0 Task 7<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour 2<\/strong><\/td>\nArtificial Neural Networks (ANNs)<\/td>\nDetails may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nActivation functions<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nForward and backward propagation<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nConvolutional Neural Networks (CNNs)<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nPooling layers<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nConvolutional layers<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nBatch Normalization<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nRecurrent Neural Networks (RNNs)<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nLong Short-Term Memory (LSTM) networks<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nGated Recurrent Units (GRUs)<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nWord-level language modeling<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nAutoencoders<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nVariational Autoencoders (VAEs)<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nGenerative Adversarial Networks (GANs)<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDeep Reinforcement Learning (DRL) with DL<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nTransfer Learning<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour 2<\/strong><\/td>\nFine-tuning and feature extraction<\/td>\n<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nDomain adaptation<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nPractical applications of DL<\/td>\n<\/tr>\n
Week 8<\/strong><\/td>\nRobotics Control<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Robotics Control<\/td>\n\u00a0<\/strong><\/p>\n

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\u00b7\u00a0 Task 8<\/strong><\/p>\n

Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nDegrees of freedom and joint types<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nForward kinematics<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nInverse kinematics<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nDifferential kinematics<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nJacobians and manipulability<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nControl of robot arms<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nInverse dynamics<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nRobot dynamics<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nLagrangian dynamics<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nNewton-Euler equations<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nRobust control of robots<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nTrajectory planning and control<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour 2<\/strong><\/td>\nMotion planning algorithms<\/td>\n<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nPath following control<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nFeedback linearization<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nPractical applications of robotics control<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nEmerging trends in robotics control<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nRobotics control<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nRevision of complete topic<\/td>\n<\/tr>\n
Week 9<\/strong><\/td>\nReinforcement <\/strong>Learning for Robotics<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Reinforcement Learning for Robotics<\/td>\n\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

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\u00a0<\/strong><\/p>\n

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\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00b7\u00a0 Task 9<\/strong><\/p>\n

Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nRobotics Applications of RL<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nMarkov Decision Processes (MDPs)<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nMarkov Decision Processes (MDPs)<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nQ-Learning<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nDeep Q-Learning<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nExperience Replay<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDiscussion session<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\nDay 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nPolicy Gradient Methods<\/td>\n<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nActor-Critic Methods<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nProximal Policy Optimization (PPO)<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nProximal Policy Optimization (PPO)<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nMulti-Agent RL<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nDecentralized and Centralized RL<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nCooperative and Competitive RL<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDiscussion<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nRL for Robotics Case Studies<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nIndustrial Automation<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nIndustrial Automation<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nAutonomous Driving<\/td>\n<\/tr>\n
Week <\/strong>10<\/strong><\/td>\nAdvanced Computer Vision<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Advanced Computer Vision<\/td>\n\u00b7 Task 10<\/strong><\/p>\n

Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nObject Detection<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nObject Tracking<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDiscussion<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\nDay 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nSemantic Segmentation<\/td>\n<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nInstance Segmentation<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nMask R-CNN<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDiscussion<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nGenerative Models<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nVariational Autoencoders<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nGenerative Adversarial Networks (GANs)<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDiscussion<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nVideo Understanding<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nOptical Flow<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nAction Recognition<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nAction Recognition<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\n3D Computer Vision<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nMonocular Depth Estimation<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nRGB-D Reconstruction<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nComplete topic revision<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Week <\/strong>11<\/strong><\/td>\nDeep <\/strong>Reinforcement Learning<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Deep Reinforcement Learning (DRL)<\/td>\n\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

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\u00a0<\/strong><\/p>\n

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\u00a0<\/strong><\/p>\n

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\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

\u00b7\u00a0 Task 11 <\/strong>Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nDRL Frameworks<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nDRL Frameworks<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDQN Revisited<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nDeep Policy Gradient Methods<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nREINFORCE<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nActor-Critic Methods<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDiscussion<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nAsynchronous RL<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nAsynchronous RL<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nA3C<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDistributed RL<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nExploration Strategies<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nEpsilon Greedy<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nBoltzmann Exploration<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour 4<\/strong><\/td>\nDiscussion<\/td>\n<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nRL for Games<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nAtari Games<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nAlphaGo and AlphaZero<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nComplete topic revision<\/td>\n<\/tr>\n
Week <\/strong>12<\/strong><\/td>\nRobotics Perception<\/strong><\/td>\nDay 1<\/strong><\/td>\nHour 1<\/strong><\/td>\nIntroduction to Robotics Perception<\/td>\n\u00b7 Task 12<\/strong><\/p>\n

Details may<\/u><\/em> be seen at<\/u> Annexure-I<\/u><\/em><\/p>\n

\u00a0<\/strong><\/p>\n

Final Project<\/strong><\/td>\n<\/tr>\n

Hour 2<\/strong><\/td>\nSensors in Robotics<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nSensors in Robotics<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nCameras<\/td>\n<\/tr>\n
Day 2<\/strong><\/td>\nHour 1<\/strong><\/td>\nDepth Perception<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nStereo Vision<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nTime of Flight (ToF)<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nTime of Flight (ToF)<\/td>\n<\/tr>\n
Day 3<\/strong><\/td>\nHour 1<\/strong><\/td>\nLiDAR<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nTypes of LiDAR<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nPoint Cloud Processing<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/td>\n<\/td>\nHour 4<\/strong><\/td>\nPoint Cloud Processing<\/td>\n<\/td>\n<\/tr>\n
Day 4<\/strong><\/td>\nHour 1<\/strong><\/td>\nSimultaneous Localization and Mapping (SLAM)<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nTypes of SLAM<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nVisual SLAM<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nDiscussion<\/td>\n<\/tr>\n
Day 5<\/strong><\/td>\nHour 1<\/strong><\/td>\nRobotics Perception Case Studies<\/td>\n<\/tr>\n
Hour 2<\/strong><\/td>\nSelf-Driving Cars<\/td>\n<\/tr>\n
Hour 3<\/strong><\/td>\nAutonomous Drones<\/td>\n<\/tr>\n
Hour 4<\/strong><\/td>\nApplications in daily life<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Tasks for Certificate in AI (Robotics)<\/h2>\n

\u00a0<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Task No.<\/strong><\/td>\nTask<\/strong><\/td>\nDescription<\/strong><\/td>\nWeek<\/strong><\/td>\n<\/tr>\n
1.<\/strong><\/td>\nSimple robot<\/strong><\/td>\nBuild a simple robot using a kit<\/td>\nWeek 1<\/strong><\/td>\n<\/tr>\n
2.<\/strong><\/td>\nBasic coding<\/strong><\/td>\nWrite a program to control the robot built in Week 1<\/td>\nWeek 2<\/strong><\/td>\n<\/tr>\n
3.<\/strong><\/td>\nMachine Learning implementation<\/strong><\/td>\nImplement a simple ML model to make the robot move based on data from its sensors<\/td>\nWeek 3<\/strong><\/td>\n<\/tr>\n
4.<\/strong><\/td>\nComputer Vision Implementation<\/strong><\/td>\nBuild a program to detect and track objects using a camera<\/td>\nWeek 4<\/strong><\/td>\n<\/tr>\n
5.<\/strong><\/td>\nChatbot<\/strong><\/td>\nBuild a chatbot that can answer simple questions<\/td>\nWeek 5<\/strong><\/td>\n<\/tr>\n
6.<\/strong><\/td>\nRobot (RL technique)<\/strong><\/td>\nBuilding a robot that can navigate through a maze using RL techniques<\/td>\nWeek 6<\/strong><\/td>\n<\/tr>\n
7.<\/strong><\/td>\nImplement DL model<\/strong><\/td>\nImplement a simple DL model to recognize objects in images<\/td>\nWeek 7<\/strong><\/td>\n<\/tr>\n
8.<\/strong><\/td>\nRobotic Arm<\/strong><\/td>\nBuild a program to control a robot arm<\/td>\nWeek 8<\/strong><\/td>\n<\/tr>\n
9.<\/strong><\/td>\nBuilding a robot that can learn to perform tasks through RL<\/strong><\/td>\nBuild a robot that can learn to perform tasks through RL<\/td>\nWeek 9<\/strong><\/td>\n<\/tr>\n
10.<\/strong><\/td>\nComputer Vision<\/strong><\/td>\nBuild a program to detect and track objects in real-<\/p>\n

time using a camera<\/td>\n

Week10<\/strong><\/td>\n<\/tr>\n
11.<\/strong><\/td>\nDRL Techniques<\/strong><\/td>\nBuild a robot that can learn to perform complex tasks using DRL techniques<\/td>\nWeek11<\/strong><\/td>\n<\/tr>\n
12.<\/strong><\/td>\nLiDAR and SLAM<\/strong><\/td>\nBuilding a program to map a room using LiDAR and SLAM techniques<\/td>\nWeek12<\/strong><\/td>\n<\/tr>\n
13.<\/strong><\/td>\nFinal Project<\/strong><\/td>\nCombining all the topics covered in the course to build a complete AI-driven robot that can perform tasks autonomously.<\/td>\nWeek12<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Annexure-<\/em><\/strong>II:<\/em><\/strong><\/p>\n

Motivational Lectures AI (Robotics)<\/h2>\n

The Rise of AI: https:\/\/www.youtube.com\/watch?v=5J5bDQHQR1g<\/h3>\n

This video provides an overview of the impact that AI is having on various industries and highlights some of the breakthroughs that have been made in recent years.<\/p>\n

 <\/p>\n

 <\/p>\n

How Robotics Will Change the World: https:\/\/www.youtube.com\/watch?v=UwsrzCVZAb8<\/h3>\n

This video provides an overview of the impact that robotics is having on society, including in fields such as healthcare, manufacturing, and agriculture.<\/p>\n

 <\/p>\n

 <\/p>\n

What is Deep Learning and How Does it Work? : https:\/\/www.youtube.com\/watch?v=aircAruvnKk<\/h3>\n

This video provides a motivational introduction to deep learning, explaining what it is and how it works, as well as some of the applications of deep learning.<\/p>\n

 <\/p>\n

 <\/p>\n

The Promise and Peril of Our Machine Learning Future: https:\/\/www.youtube.com\/watch?v=I-JfN9HNmV4<\/h3>\n

This video provides an overview of the potential benefits and risks of machine learning, and how it will impact the future of society.<\/p>\n

 <\/p>\n

 <\/p>\n

The Future of Robotics: https:\/\/www.youtube.com\/watch?v=w22b-E_qP5o<\/h3>\n

This video provides an exciting look at the future of robotics, including how robots will impact various industries and the potential for robots to become a part of our daily lives.<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=”full_width_content” full_screen_row_position=”middle” column_margin=”default” equal_height=”yes” content_placement=”top” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” top_padding=”4%” bottom_padding=”4%” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” class=”contact-form-with-img” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_color=”#406f5b” shape_divider_position=”bottom” shape_divider_height=”100″ shape_divider_bring_to_front=”true” bg_image_animation=”none” shape_type=”waves”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” zindex=”-999999999″ gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”5\/12″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][image_with_animation image_url=”2806″ animation=”Fade In” hover_animation=”none” alignment=”” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default” el_class=”img-side-contact”][\/vc_column][vc_column column_padding=”padding-6-percent” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”left-right” top_margin_phone=”4%” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_shadow=”none” column_border_radius=”none” column_link_target=”_self” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”7\/12″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][vc_custom_heading text=”Quick Inquiry” font_container=”tag:h2|font_size:36|text_align:left|color:%23000000|line_height:1.2″ google_fonts=”font_family:Montserrat%3Aregular%2C700|font_style:700%20bold%20regular%3A700%3Anormal” css=”.vc_custom_1677876449507{margin-bottom: 3% !important;padding-top: 4% !important;}” el_id=”contact”][vc_column_text]\n

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