July 4th to July 10th
- Registrations started
- Reading material sent to applicants
July 11th to July 17th
- Basics of Machine Learning and Concepts
- Theory of SGD, Linear Regession
- Theory of Logistic Regession
- Programming regression from scratch
- Project on Linear Regression
- Project on Logistic Regression
- Types of ML Techniques
July 18th to July 24th
- Intro to Unsupervised Learning
- Ways to do Unsupervised Learning
- Clustering, K Means
- Support Vector Machines
- Decision trees
- Problems with Unsupervised Learning
- Where to apply Unsupervised Learning
July 25th to July 31st
- Intro to Supervised Learning
- Why it works? How it works.
- Basics of Probablity
- Fuzzy logic, soft computing
- Intro to MATLAB/Octave
- Problems in MATLAB
Aug 1st to Aug 7th
- Intro to Neural Computation
- Intro to Neural Networks
- Activation functions
- Program a neuron in MATLAB
- Layers of Neurons, order out of chaos
- Program a neural layer in MATLAB
- Single layer NN & SGD
- Multi layer NN & gradient falloff
- Need for backpropagation
Aug 8th to Aug 14th
- Tensor math and logic
- Intro to PyTorch
- Make a simple NN with PyTorch
- Intro to Tensorflow & Keras
- Make a simple NN with Keras
- Jupyter notebooks, Kernels
- Kaggle notebooks, Google Collab
Aug 15th to Aug 21st
- Neural Network Training
- Weights and Cost functions
- Loss metric and accuracy
- Types of Loss, cross-entropy
- Hyperparameters and tuning
- Overfitting and local minimas
- Optimizers in NNs
Aug 22nd to Aug 28th
- Neural Network Architectures
- Feed forward networks
- Recurrent networks
- Convolutional networks
- Adversarial networks
Aug 29th to Sep 4th
- Recurrent Neural networks
- Language models with RNNs
- Predictions with RNNs
Sep 5th to Sep 11th
- Convolutional Neural networks
- Image processing with CNNs
- Image classification with CNNs
- Dimensionality reduction
- CNNs over RNNs, When to use?
Sep 12th to Sep 18th
- Adversarial networks
- Generative Adversarial Networks
- Transformers (GPT)