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\begin{document}

\noindent One point per item if you know precisely the meaning of the
listed word(s)

\section{Machine Learning}

\begin{enumerate}

  \item VC dimension
  \item over-fitting, under-fitting
  \item logistic regression
  \item Q-value
  \item kernel trick
  \item boosting
  \item feature design
  \item linear regression
  \item expectation-maximization, GMM
  \item SVM
  \item Bellman equation
  \item decision tree
  \item train/validation/test sets
  \item naive Bayesian model
  \item autoregressive model
  \item bias-variance dilemma
  \item policy gradient
  \item random forest
  \item k-NN
  \item perceptron algorithm

\end{enumerate}


\section{Deep-Learning}

\begin{enumerate}

  \item Adam
  \item softmax
  \item residual connections
  \item autograd
  \item ReLU
  \item dropout
  \item CLIP
  \item Xavier's initialization
  \item Vanishing gradient
  \item LeNet
  \item ViT
  \item transposed convolution layer
  \item checkpoint (during the forward pass)
  \item minibatch
  \item masked model
  \item supervised / unsupervised
  \item data augmentation
  \item attention block
  \item SGD
  \item batchnorm
  \item gradient clipping
  \item tokenizer
  \item VAE
  \item weight decay
  \item GELU
  \item LSTM, GRU
  \item GAN
  \item resnet
  \item straight-through estimator
  \item convolution layer
  \item pre-training / fine-tuning
  \item perplexity
  \item logits
  \item cls token
  \item forward pass
  \item Transformer (original one), GPT
  \item backward pass
  \item autoencoder, denoising autoencoder
  \item layer norm
  \item GNN
  \item diffusion model
  \item cross-entropy
  \item max pooling, average pooling
  \item RNN
  \item contrastive loss
  \item positional encoding
  \item causal model
  \item attention layer
  \item SSL
  \item MSE
  \item positional encoding
  \item tensor

\end{enumerate}

\section{Math}

\begin{enumerate}
  \item Hessian
  \item random variable
  \item matrix
  \item entropy, mutual information
  \item dot product
  \item mean, variance
  \item L2 norm
  \item chain rule (differentiation)
  \item Fourier transform
  \item continuity, Lipschitz continuity
  \item chain rule (probability)
  \item polynomial
  \item Cantor's diagonal argument
  \item Jacobian
  \item linear operator
  \item gradient
  \item Bayes' thorem
  \item vector
  \item joint law, product law
  \item Gaussian distribution
  \item distribution
  \item determinant, rank
  \item eigen-decomposition, svd
  \item maximum likelihood
  \item Central Limit Theorem

\end{enumerate}

\section{Computer Science}

\begin{enumerate}

%% \itemsep0em 

  \item polymorphism
  \item recursion
  \item value passed by reference
  \item binary search
  \item quick sort
  \item parallel scan
  \item mutability
  \item Turing machine
  \item FP32
  \item iterator
  \item interpreter, compiler
  \item anonymous function
  \item set
  \item binary heap
  \item mutex
  \item cache memory
  \item scope of a variable or function
  \item dynamic programming
  \item hash table
  \item big-O notation
  \item Turing complete
  \item class inheritance
  \item closure
  \item loop unrolling
  \item complexity

\end{enumerate}

\end{document}
