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10 \noindent One point per item if you know precisely the meaning of the
13 \section{Machine Learning}
18 \item over-fitting, under-fitting
19 \item logistic regression
24 \item linear regression
25 \item expectation-maximization, GMM
27 \item Bellman equation
29 \item train/validation/test sets
30 \item naive Bayesian model
31 \item autoregressive model
32 \item bias-variance dilemma
36 \item perceptron algorithm
41 \section{Deep-Learning}
47 \item residual connections
52 \item Xavier's initialization
53 \item Vanishing gradient
56 \item transposed convolution layer
57 \item checkpoint (during the forward pass)
60 \item supervised / unsupervised
61 \item data augmentation
65 \item gradient clipping
73 \item straight-through estimator
74 \item convolution layer
75 \item pre-training / fine-tuning
80 \item Transformer (original one), GPT
82 \item autoencoder, denoising autoencoder
87 \item max pooling, average pooling
89 \item contrastive loss
90 \item positional encoding
95 \item positional encoding
105 \item random variable
107 \item entropy, mutual information
111 \item chain rule (differentiation)
112 \item Fourier transform
113 \item continuity, Lipschitz continuity
114 \item chain rule (probability)
116 \item Cantor's diagonal argument
118 \item linear operator
122 \item joint law, product law
123 \item Gaussian distribution
125 \item determinant, rank
126 \item eigen-decomposition, svd
127 \item maximum likelihood
128 \item Central Limit Theorem
132 \section{Computer Science}
138 \item value passed by reference
146 \item interpreter, compiler
147 \item anonymous function
152 \item scope of a variable or function
153 \item dynamic programming
156 \item Turing complete
157 \item class inheritance