1 %% -*- mode: latex; mode: reftex; mode: flyspell; coding: utf-8; tex-command: "pdflatex.sh" -*-
3 \documentclass[11pt,a4paper,twocolumn,twoside]{article}
4 \usepackage[a4paper,top=2cm,bottom=2cm,left=2.5cm,right=2.5cm]{geometry}
5 \usepackage[utf8]{inputenc}
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
25 \item linear regression
26 \item expectation-maximization, GMM
28 \item Bellman equation
30 \item train/validation/test sets
31 \item naive Bayesian model
32 \item autoregressive model
33 \item bias-variance dilemma
37 \item perceptron algorithm
42 \section{Deep-Learning}
48 \item residual connections
53 \item Xavier's initialization
54 \item Vanishing gradient
57 \item transposed convolution layer
58 \item checkpoint (during the forward pass)
61 \item supervised / unsupervised
62 \item data augmentation
66 \item gradient clipping
74 \item straight-through estimator
75 \item convolution layer
76 \item pre-training / fine-tuning
81 \item Transformer (original one), GPT
83 \item autoencoder, denoising autoencoder
86 \item learning rate schedule
89 \item max pooling, average pooling
91 \item contrastive loss
92 \item positional encoding
106 \item random variable
108 \item entropy, mutual information
112 \item chain rule (differentiation)
113 \item Fourier transform
114 \item continuity, Lipschitz continuity
115 \item chain rule (probability)
117 \item Cantor's diagonal argument
119 \item linear operator
123 \item joint law, product law
124 \item Gaussian distribution
126 \item determinant, rank
127 \item eigen-decomposition, svd
128 \item maximum likelihood
129 \item Central Limit Theorem
133 \section{Computer Science}
139 \item value passed by reference
147 \item interpreter, compiler
148 \item anonymous function
153 \item scope of a variable or function
154 \item dynamic programming
157 \item Turing complete
158 \item class inheritance