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RDA 212 - AI for humanoid robots

RDA 211 - Advanced Control theory for mobile robots

RDA 113 - Control theory and IOT

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RDA 112 - Practical electronics and embedded systems

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This is a detailed course on:
2D and 3D Design
3D Printing and CNC Machining
Parts Fabrication
Design Thinking
Product t Development Life Cycle
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RDA 111 Product Design and Development

RDA 102 - Industrial automation

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course details to be uploaded
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PPM 101: Project Management

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course details to be uploaded
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Friday for Web Development 

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Week 1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Introduction to Neural nets
Week 2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Training deep neural networks: activation functions and loss functions
Week 3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Training deep neural networks: optimisers, dropouts and regularization
Week 4.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Tensorflow and Keras for Deep learning
Week 5.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Convolutional Neural Networks
Week 6.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Computer vision using OpenCV
Week 7.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Transfer learning
Week 8.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; YOLO
Week 9.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Semantic Segmentation
Week 10.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Object recognition and detection tasks
Week 11.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Siamese networks
Week 12.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Introduction to Natural Language processing: text extraction, bag of words, TF-IDF, Word2Vec, and&nbsp; GloVe
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AIML 212 - Deep learning

AIML 211 - Machine learning II

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<h1>Course Outline</h1>
Week 1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Linear regression
Week 2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Logistic Regression
Week 3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Support Vector Machines
Week 4.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Naive Bayes Model
Week 5.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; K- Nearest Neighbour and Decision Tree models
Week 6.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Model Assessment and Cross Validation
Week 7.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Ensemble Techniques
Week 8.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Unsupervised learning: K-Means Clustering
Week 9.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Unsupervised Learning: Hierarchical Clustering
Week 10.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Principal Component Analysis
Week 11.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Model Defects and Evaluation
Week 12.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Pipelines, Transformers and Custom Estimators
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