Course Title: Training on Al for Immersive Technology
COURSE 1:AI and Machine Learning (ML) with Python
Module A: Python Programming
Lesson Plan
| Lecture | Topic | Teaching-Learing Methodology | Assessment | Hours |
|---|---|---|---|---|
| Lecture: 1-2 | Preparing Machine and environment Set up -Fundamentals of Python:·Introduction to python ·Writing python code ·Running python code Working with different types of data in python:·Data types and variables ·Using numeric value Using string variables | ·Lecture on theoretical background ·Hands on demonstration on implementation | quiz | 03 |
| Lecture: 3-4 | Input & output methods in python:·Printing with parameters ·Getting input from users ·String formatting Simple and complex decisions making using “if-else”statement:·The “if" Statement · Logical Operators ·More Complex Expressions | ·Lecture on theoretical background Hands on demonstration on implementation | Tests,quiz | 03 |
| Lecture: 5-6 | Implement different types of loops and practice associated problems:·“for”loops·“while”loopsAdvanced data storage technique in python: | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| ·Indexing in list and dictionary ·Create,update and delete list and dictionary elements ·Perform basic operations on list and dictionary elements | ||||
| Lecture: 7-8 | Learn about different string functions and implement them:·String input methods ·Manipulate strings ·Built-in string functions | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 9-10 | Implement basic I/O functions:·Opening and closing files ·Different modes of accessing files ·Create,update and delete a file | ·Lecture on theoretical background Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture:11-12 | -Different types of data analysis using Python -Data visualization and explainability of data for decision making | ·Hands on demonstration on implementation | Tests,quiz | 03 |
| Lecture:13-14 | Evaluation | Test,quiz,exam,project implementation | Test,quiz,exam,project implementation | 02 |
| Total(Hrs) | Total(Hrs) | Total(Hrs) | 20 |
Module B:AI and Machine Learning
Lesson Plan
| Lecture | Topic | Teaching-Learning Methodology | Assessment | Hou rs |
|---|---|---|---|---|
| Lecture: 1-2 | Introduction of AI& ML,History of AI, Weak and Strong AI, AI and Its Applications, AI+MLCurrent & Future Trends, Prospects of AI+ML,NecessarySkills for learning AI+ML | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz | 03 |
| Lecture: 3-4 | Intelligent Agents,Uninformed Search,Informed Search, Heuristic Search | ·Lecture on theoretical background ·Hands on demonstration on | Tests,quiz, assignment | 03 |
| implementation | ||||
| Lecture: 5-6 | Game AI (Mini-max & alpha-beta pruning,Constraint Satisfaction Problem | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 7-8 | Propositional & Predicate Logic, Planning,Natural Language Processing, Frame Problem | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 02 |
| Lecture: 9-10 | Difference between AI&ML,ML Applications,Importance of AI+ML on Industry 4.0 | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture:11-12 | Prediction problem in ML, Classification problems in ML, Clustering problems in ML, AI &ML Tools, Libraries,Software | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz | 02 |
| Lecture: 13-14 | Linear algebra,Statistics Probability theory | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz | 03 |
| Lecture: 15-16 | Data processing, cleaning, and manipulation,exploratory data analysis | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture:17-18 | Branches of ML:·Supervised learning · Unsupervised learning .Reinforcement learning | ·Lecture on theoretical background ·Hands on demonstration on | Tests,quiz | 03 |
| implementation | ||||
| Lecture: 19-20 | Evaluation 1 | Test,quiz,exam,project implementation | Test,quiz,exam,project implementation | 03 |
| Lecture:21-22 | Linear regression · Gradient descent ·Loss computation ·Evaluation Metrics - Solving a problem with linear regression | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 23-24 | Logistic regression ·Hypothesis representation · Cost function · Advanced optimization - Solving a problem with logistic regression | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture:25-26 | Data preparation and feature extraction · Vectorization · Computing on data ·Plotting on data | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture:27-28 | Support vector machines · Optimization · Large margin intuitions · Kernels Overfitting & Underfitting ·Reducing network size ·Adding weight regularization · Adding dropout | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture:29-30 | Multinomial Naïve Bayes, Stochastic Gradient Descent, Decision Tree,Random forest | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 31-32 | Unsupervised Learning · K-means · KNN . PCA · SVD · ICA | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 33-34 | Evaluating ML Models Training ·Validation·Testing·Performance matrices ·ML Tools & library packages | ·Lecture on theoretical background Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 35-36 | ML Applications in NLP ·Feature extraction (TF-IDF, BoW) ·Model Development: Training, testing ·Classification & Prediction ·Error analysis | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, Project | 03 |
| Lecture: 37-38 | ML Applications in Computer Vision ·Visual Feature extraction ·Feature visualization ·Model Interpretation ·Model training and testing | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, Project | 03 |
| Lecture:39-40 | ML-based Project development ·Image Classification ·Character Recognition ·Text Classification ·Face Recognition ·Weather Prediction ·Sentiment Analysis ·Brand monitoring | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, Project | 03 |
| Lecture 41-42 | Importance of Data on AI-ML based system,The Future with AI,AI Issues,Concerns &Ethical Considerations | ·Lecture·Examples | Tests,quiz | 03 |
| Lecture: 43-44 | Evaluation 2 | Test,quiz, exam, project implementation | Test,quiz, exam, project implementation | 03 |
| Total (Hrs) | Total (Hrs) | Total (Hrs) | Total (Hrs) | 66 |
COURSE 2: Deep Learning
Lesson Plan
| Lecture | Topic | Teaching-Learning Methodology | Assessment | Hou rs |
|---|---|---|---|---|
| Lecture: 1-2 | Why DL,Difference between ML and DL,Real-world applications of DL,Popular DL techniques | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz | 03 |
| Lecture: 3-4 | DL Tools and library, Set up of DL frameworks,Experience with Tensorflow/Keras libraries, Google colab | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz | 03 |
| Lecture:5-6 | Data preparation ·Data accumulation, Data cleaning,noise removal,Data annotation ·Annotation quality measures with Kappa, ·Numeric mapping | ·Lecture on theoretical background Hands on demonstration on implementation | Tests,quiz, project | 03 |
| Lecture:7-8 | Manual labelling vs.automatic labelling -Automatic labelling techniques | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz | 03 |
| Lecture:9-10 | Feature extraction ·Understanding the data ·Extracting the textual,visual, speech features ·Normalization of features ·Features fusion | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture:11-12 | Visualization of word vectors with Word Cloud,histogram, heatmap, Plots, Tableau | ·Lecture on theoretical background Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 13-14 | Embedding Models ·Word representation ·Embedding matrix ·Word2Vec,FastText and Glove | ·Lecture on theoretical background ·Hands on demonstration on | Tests,quiz, project | 03 |
| implementation | ||||
| Lecture: 15-16 | Evaluation 1 | Test,quiz, exam,project implementation | Test,quiz, exam,project implementation | 03 |
| Lecture: 17-18 | Pre-trained word embedding ·Implications of pre-trained word vectors ·Tuning the word vectors ·Embedding model (Intrinsic & Extrinsic) evaluation | ·Lecture on theoretical background Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 19-20 | ANN & CNN ·Network design ·Convolution operation ·Max-pooling operation ·Building network ·Training, testing,valiation | Lecture on theoretical background Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 21-22 | CNN Variations:AlexNet,VGG-16,VGG-19,GoogLeNet, ResNet-18, ResNet-34,ResNet-50,ResNet-101, ResNet-152 MobileNet | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 23-24 | CNN Variations:ResNet-18,ResNet-34,ResNet-50,ResNet-101,ResNet-152,MobileNet | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture: 25-26 | Optimization of hyperparameters ·Understanding parameters and hyperparameters ·Tuning hyperparameters ·Effect of hyperparameter tuning | Lecture on theoretical background Hands on demonstration on implementation | Tests,quiz, project | 03 |
| Lecture:27-28 | Recurrent neural networks ·Backpropagation·Why RNNs ·Vanishing gradient in RNNs ·GRU,LSTM·Bidirectional RNNs | ·Lecture on theoretical background ·Hands on demonstration on implementation | Tests,quiz, assignment | 03 |
| Lecture:29-30 | Ensemble of DL Models -Why ensemble? | ·Lecture on theoretical | Tests,quiz | 03 |
| -How to ensemble?-Average ensemble -Weighted ensemble -Voting ensemble | background ·Hands on demonstration on implementation | |||
| Lecture:31-32 | Project development using DL -Handwritten character/digitrecognition -Image classification -Object recognition -Face detection | Lecture on theoretical background ·Hands on demonstration on implementation | Quiz, Project | 03 |
| Lecture: 33-34 | Project development using DL -Language modelling -Recommender system -Sentiment analysis -Emotion Analysis -Text classification -Aggressive text detection -Multimodal meme detection | ·Lecture on theoretical background Hands on demonstration on implementation | Quiz, Project | 03 |
| Lecture 35-36 | Introduction to transformer-based models Why use transformer-base models?Transformer vs. DL models Design of m-BERT, distil-BERT, XLM-R,RoBERTa | ·Lecture on theoretical background Hands on demonstration on implementation | Test,quiz | 03 |
| Lecture 37-38 | Evaluation of DL models -Performance matrices -Error analysis | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 39-40 | Evaluation 2 | Test,quiz, exam,project implementation | Test,quiz, exam,project implementation | 03 |
| Total(Hrs) | Total(Hrs) | Total(Hrs) | Total(Hrs) | 60 |
COURSE 3: Training on Augmented Reality (AR), Virtual Reality (VR),Mixed Reality (MR)and Extended Reality (XR)
Lesson Plan
| Lecture | Topic | Teaching-Learning Methodology | Assessmen t | Hours |
|---|---|---|---|---|
| Lecture 1-2 | Introduction to Immersive Technologies -A Brief History of AR/VR/MR/XR- Components of a AR/VR/MR/XRSystems -Reality, Virtuality & Immersion | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz | 03 |
| Lecture 3-4 | -VR, AR, MR, XR: similaritis and differences -Current trends and state of the art in immersive technologies, developing platforms and consumer devices -The future of human experience | ·Lecture on theoretical background Hands on demonstration on implementation | Test,quiz | 03 |
| Lecture 5-6 | Motion tracking, navigation and controllers -Position and Motion Trackers -Inside Out/Outside In -Tracker Performance Parameters -Optical - Active and Passive Trackers -Inertial and Hybrid Trackers -HMD Trackers -Magnetic Trackers -Mechanical Trackers -Ultrasonic Trackers | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz | 03 |
| Lecture 7-8 | -HMD Trackers -Magnetic Trackers -Mechanical Trackers -Ultrasonic Trackers -Laser Sensors, Vision Sensors -Control devices | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 9-10 | -Navigation and Manipulation Interfaces -Tracker-Based Navigation/Manipulation Interfaces -Three-Dimensional Probes and Controllers -Data Gloves and Gesture Interfaces | ·Lecture on theoretical background Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 11-12 | The Human behind the lenses -Human Perception and Cognition -The Human Visual System -The Human Auditory System -The Human Vestibular System -Physiology,Psychology and the Human Experience | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 13-14 | -Adaptation and Artefacts -Ergonomics | ·Lecture on theoretical background | Test,quiz, assignment | 03 |
| -Ethics -Scientific Concerns -VR Health and Safety Issues -Effects of VR Simulations on Users -Cybersickness, before and now | ·Hands on demonstration on implementation | |||
| Lecture 15-16 | -Guidelines for Proper VR Usage -User-cantered Design, User Experience and an Ethical Code of Conduct | ·Lecture on theoretical background Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 17-18 | Emergence of XR in the Workplace -Areas and industries for immersive reality applications -Entertainment -Education -Training -Medical -Industrial -Military | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz | 03 |
| Lecture 19-20 | -Use-cases, applications and production pipelines -From Sensing to Rendering -Mobile,Standalone and high-end immersive computing platforms -VR, Immersive Tech and the Society -Impact on Professional Life -Impact on Private Life -Impact on Public Life | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz | 03 |
| Lecture 21-22 | Camera tracking and 3D Rendering for Immersive Environments ·Inside-Out Camera tracking -Depth Sensing -Microsoft HoloLens -Vrvana Totem -Low cost AR and MR systems -Mobile Platforms ·Full-Body tracking -Inverse & Forward Kinematics -Kinect -Intel Realsense -Full body inertial tracking -Ikinema | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| -Holographic Video ·Rendering Architecture -Graphics Accelerators, -3D Rendering API's, OpenGL, DirectX,Vulcan,Metal, -Best practices and Optimization techniques ·Distributed VR Architectures -Multi-pipeline Synchronization -Co-located Rendering Pipelines -Distributed Virtual Environments | ||||
| Lecture 23-24 | Modelling the Physical World ·Geometric Modelling -Virtual Architecture -Virtual Object Shape -Virtual Object Appearance -Procedural Textures -Advanced Material Properties -Procedural Objects -Photogrammetry ·Kinematics Modelling -Homogeneous Transformation Matrices -Object Position -Transformation Invariants -Object Hierarchies -Scale,Perspective and Perception -Physical Modelling -Collision Detection -Surface Deformation -Force computation -Force Smoothing and Mapping -Haptic Texturing ·Behaviour Modelling ·Model Management -Level-d-Detail Management -Cell Management | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 25-26 | Sound in Immersive Environments ·Evolution of Sound Systems -From mono to stereo to surround -Object Based Sound -Ambisonics -HRTF ·Sound Design Basics -Sound as Information | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| -Earcons -Impact of Sound in Objects and Actions -Natural vs Real Sound | ||||
| Lecture 27-28 | Familiarity with Unity Engine,Set up and running the applications | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 29-30 | Development with Unity -Build Interactivity with Timeline -Create Animated Sories with Unit -Create Compelling Shots with Cinemachine | Lecture on theoretical background Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 31-32 | -Create High-Fidelity Lighting in the High-Definition Render Pipeline -Create Real-Time Visualizations with Unity -DOTS (Data-Oriented Technology Stack) Fundamentals -Data-Oriented Design | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 33-34 | Develop 3D Mobile Games Develop Interactive User Interfaces in Unity Develop Mobile AR Applications | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 35-36 | Develop VR & XR Applications with Unity,Unreal Engines and the XR Interaction Toolkit | ·Lecture on teoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 37-38 | Introduction to Mixed Reality (MR) -Explore MR devices -Understand holograms -Design and develop in MR -Use cases and examples -MR cloud services and applications -Introduction to the MR Toolkit--Set Up Project & Use Hand Interaction -Configure Windows MR -Import and configure resources -Interaction models -Add hand interaction scripts to an | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| object | ||||
| Lecture 39-40 | Types of MR apps & Hardware -Enhanced environment apps (HoloLens only) -Blended environment apps -Immersive environment apps -Techniques for expanding the design process -MR Hardware:HoloLens 2, Immersive headset | ·Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 41-42 | Designing Holograms -Designing for mixed reality -Exploring the doll house -1:1 vs 1:10 prototypes -Using Mixed Reality Capture -Manipulating captures and virtual objects -Head Gaze Adjustment -Syncing Animated Objects -UI creative process | ·Lecture on theoretical background Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 43-44 | Design & Develop MR Applications -Structural elements: App model, coordinate systems, spatial mapping,scene understanding -Interactions: system gesture, instinctual interaction, hands &motion controllers model,hand-free model,eye-based interaction -UX elements: Visual, spatial sound,controls and behaviours | Lecture on theoretical background ·Hands on demonstration on implementation | Test,quiz, assignment | 03 |
| Lecture 45-46 | Evaluation | Test,quiz, exam,project implementation | Test,quiz, exam,project implementation | 04 |
| Total Hours | Total Hours | Total Hours | Total Hours | 70 |
Course 4: Communicative English
| Month 1 | Month 1 |
|---|---|
| Week | Topics/Session titles |
| Week 1 | Class 1:Introductory and ice breaking session, class rules,motivations,theoretical and practical work-based briefing, to do and not to do list forthis course Class 2:Introducing 4 modules and assessing their expectations Class 3: Introducing with new people,times and greetings practice |
| Week 2 | Class 4:Pronunciation practice Class 5: Modulation, Intonation practice Class 6:Formal and informal conversation practice |
| Week 3 | Class 7: How to write a latest and persuasive CV and job applicationClass 8: Formal and informal email writing Class 9:Use of tense and parts of speech for professional correspondence |
| Week 4 | Class 10:Reading comprehension and finding out the jargon of ICT, CSE,Internet, Wi-Fi, digitalization Class 11: Reading techniques: Skimming, scanning, and other techniquesClass 12:Techniques of faster reading |
| Month 2 | Month 2 |
| Week 5 | Class 13:Listening (Practical from easy task of Cambridge IELTSmaterials) understanding primary information Class 14:Conversational listening Class 15:Listening practice based on the level of participants |
| Week 6 | Class 16: Speaking practical: Role play and conversationClass 17:Practicingjob interview in English (Role play) Class 18: Practicing job interview in English(Role play) |
| Week 7 | Class 19:Understanding phonetics Class 20:Using phonetics in conversation Class 21: Understanding various English accent |
| Week 8 | Class 22: Describing objects, picture,building |
| Class 23:Describing objects,picture,building Class 24:English Story telling | |
| Month 3 | Month 3 |
| Week 9 | Class 25: English debate Class 26:EnglishStory telling Class 27:English Debate |
| Week 10 | Class 28: Writing job application practicalClass 29:Writingjob application practical Class 30:Writing persuasive email letter practical |
| Week 11 | Class 31:Practicing fluency Class 32: Identifying grammatical errors in speaking using tenseClass 33:Identifyinggrammatical errors in speaking using tense |
| Week 12 | Class 34:How to create reading habit and reading comprehensionClass 35: How to create reading habit and reading comprehension Class 36: Reading world best-selling book and telling summery(HW) |
| Month 4 | Month 4 |
| Week 13 | Class 37:Situational conversation and given circumstancesClass 38: Situational conversation and given circumstances Class 39: Assessment class (Mid Mock test) |
| Week 14 | Class 40: Suffix and prefix practiceClass 41: Phrasal verb practice Class 42:Subject verb agreement |
| Week 15 | Class 43:Advance English Conversation:Using various Tense Class 44: Advance English Conversation: Using various Tense Class 45: Advance English Conversation:Using various Tense |
| Week 16 | Class 46: Synonyms,antonyms practice in writing Class 47: Using parts of speech for developing vocabularyClass 48: |
| Month 5 | Month 5 |
| Week 17 | Class 49:Topic based Speech contest practicalClass 50: Topic based Speech contest practical Class 51:Advance improvisation techniques in speaking |
| Week 18 | Class 52:Topic based writing: Importance of digitalization in a countryClass 53:Essaywriting:Self-developmentClass 54: Topic: Knowledge management |
| Week 19 | Class 55: Topic:Recent development of BangladeshClass 56:Significance of ICT Class 57:10 Proposals to ensure further development of Bangladesh |
| Week | Class 58: Understanding English lecture of Martin Luther King |
| 20 | Class 59: Under4standing persuasive lecture of Barak Obama Class 60:Audio book:Power of believing |
| Month 6 | |
| Week 21 | Class 61:Round table discussion in English (Group Work)Class 62:Roundtable discussion in English (Group Work) Class 63:Individual Speech contest |
| Week 22 | Class 64:Watching BBC documentaryClass 65:Watching 'Power of Ten' Class 66:Mock test |
| Week 23 | Class 67:Advance speaking for identifying grammatical errorsClass 68: Advance speaking for identifying grammatical errors Class 69:Developing vocabularies in speaking |
| Week 24 | Class 70: Speaking contest: open topic Class 71: Speaking contest:Given topic Class 72:Final Test |
Total Training Course Summary
| Course category | Couse title | Hours |
|---|---|---|
| COURSE 1 | AI and Machine Learning with Python | |
| Module A | Python Programming | 20 |
| Module B | Training on AI and Machine Learning | 66 |
| COURSE 2 | Training on Deep Learning | 60 |
| COURSE 3 | Training on Augmented Reality (AR), Virtual Reality (VR),Mixed Reality (MR)and Extended Reality(XR) | 70 |
| Course 4 | Communicative English | 72 |
| Total (Hours) [two hundred eighty eight hours] | Total (Hours) [two hundred eighty eight hours] | 288 |