ML Interview Prep Resources

These notes serves as a log for various important links and resources which I have used.

Classical Machine Learning

• Big fan of Killian Weinberger's funny yet highly detailed ML course
• Applied and Practical ML, This course teaches many practical skills
• Kuhn, Johnson - Applied predictive modeling
• Time Series Analysis and Its Applications Shumway, Stoffer
• Generative vs Discriminative Classifiers (Naive Bayes vs LogReg) Link
• Gradient Boosted Trees Link
• Introduction to Gradient Boosting Link
• Ensemble Methods Link
• Clustering (CA Murthy's PR Course Video # 25-27) Link
• Unsupervised Methods Link (2e, Tan et.al)
• L0, L1 and L2 regularization (Subset Selection, Lasso and Ridge Regression), a comparison. ESLR, Section 3.4.3 Link
• Perhaps everything that you'll ever need to know for the interview sake. Link (cf. mohitsharma)
• Notes I used while teaching intro ML Iteration #1 | #2 | SLR & MLR Derivations

Deep Learning

• One of the best Course on DL Link
• Speech and Language Processing Link (Jurafsky 3e)
• Why tanh for Recurrent Networks Link
• Receptive Fields in CNNs Link
• For everything Convolution Link
• Gradient Descent Link, Adaptive Learning rates in SGD Link
• Backpropagation in Python, Andrej Karpathy Link
• Colah's Fantastic Blog Link

LSTM:

1. Prerequisites for understanding RNN at a more mathematical level
2. Simple RNN: the first foothold for understanding LSTM
3. A brief history of neural nets: everything you should know before learning LSTM
4. Understanding LSTM forward propagation in two ways
5. LSTM back propagation: following the flows of variables

Transformers:

1. On the difficulty of language: prerequisites for NLP with Transformer
2. Seq2seq model and attention mechanism: a backbone of NLP with deep learning
3. Multi-head attention: the key component of Transformer
4. Positional encoding, residual connections, padding masks: covering the rest of Transformer components
5. How to make a toy English-German translator with multi-head attention heat maps: the overall architecture of Transformer

Misc

• ML Causal Inference
• All Testing you need to know, see Video # 75-86 TML 2020
• Tsne
• Uncorrelated vs Independent Random Variable Link
• SVD and PCA in real-life. Link1 Link2
• MLE Vs MAP link
• Probabilistic PCA
• Non parametric Bayes
• Amazing resource for all regression diagnostics
• Seasonal time series
• Semi supevised learning
• Using SVD for LLS
• Random Walk Metropolis algorithm
• Metropolis sampling
• Spectral density
• PC regression
• Gram schmidt
• Matrix factorization
• Diffrernce between pca, svd and vector quant
• unit root testing
• SVD numerical considerations
• Regression as tool for hypothesis testing link **
• Basics of Python OOP (If you already know OOP in other Procedural language) link

  I plan to type my notes and curate the best of best for quick review.