SUPERVISED LEARNING
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Introduction to machine learning
- Formal supervised learning setup, Linear regression, Normal equations
- Gradient descent, Hypothesis classes, Overfitting and Regularization
- Logistic regression, k-nearest neighbors
- Probability reminder, Naive bayes classifier
- Cross-validation, Bootstrapping
- Decision trees
- Tree ensemble, Random forest
- support vector machine
UNSUPERVISED LEARNING
- Intro to unsupervised learning, Liner algebra
- Eigen decomposition, SVD (singular value decomposition)
- Dimensionality reduction : PCA (principal component analysis)
- Centroid based clustering : k-means
- Centroid based clustering : k-medians, k-medoids, Hierarchical clustering
- Density based clustering : DBSCAN, OPTICS
- Distribution based clustering : GMM
- Unsupervised learning summary and review
CONVOLUTIONAL NEURAL NETWORK
- Intro to CNNs, Convolution, Correlation, Filtering
- CNN Architectures
- Detection and Segmentation
- Visualizing and Understanding
- Generative Adversarial Networks (GANs)
- Advanced CNNs for computer vision
DEEP LEARNING
- intro, history, capabilities, the perceptron
- Neural network learning : Backpropagation
- Practical network training
- Autoencoders, Batch normalization
- Why does it work? Overfitting and Generalization
ADDITIONAL METHODS, APPLICATIONS
- Intro to statistical learning, PAC learning
- No free lunch theorem, VC-dimension
- the fundamental theorem of PAC learning
- Markov model, Hidden markov model (HMM)
- HMM algorithms
ADVANCED DEEP ARCHITECTURES
- Recurrent Neural Network (RNNs)
- Advanced RNN : LSTM, GRU
- Deep reinforcement learning
- Deep learning requires rethinking generalization (google)
- Deep Super-resolution and image enhancement
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