Summaries of selected courses at ETH Zurich

If you find any mistakes, please drop me a line: [email protected]

First semester course introducing the basics. Main topics include:

• Basic properties: correctness and efficiency
• Asymptotic notation
• Divide and Conquer paradigm
• Sorting and Searching algorithms
• Data strucutres: Lists, queues, stacks, heps, (balanced) binary search trees...
• Graph algorithms: DFS, BFS, shortest paths, minimum spanning trees...

Second semester course. Concepts in Algorithms & Data Structures are expanded or rivisited, often in a slightly more theoretical/ proof oriented approach. Main topics include:

• More graph algorithms: matchings, coloring, network flows, Eulerian/ Hamilton circles, TSP...
• Introduction to discrete probability: random variables with their main properties, some notable distributions, coupon collector problem, famous inequalities and bounds (Markov, Chebishev, Chernoff).
• Randomised algorithms (Monte Carlo and Las Vegas). Examples include Min-Cuts, (short) longest paths, target shooting, hashing...
• Geometric algorithms: smallest enclosing circle, Jarvis March, convex hull...

First part of the core course on Distributed Systems. Topics include:

• Kernel
• Processes and inter-process communication
• Scheduling
• Memory Systems
• File Systems

First part of the Information Security course (core course in Theoretical Computer Science). Covered topics include:

• Basic concepts (encryption vs decryption, private vs public key cryptography, authentication...)
• Perfectly secret schemes (e.g. One Time Pad)
• Authentication alorithms and hash functions: CBC-MAC, NMAC, HMAC...
• Diffie-Hellmann and RSA algorithms
• Zero knowledge proofs
• Commitment schemes

Interdisciplinary course of the MSc Data Science Program. It covers important introductory topics in financial analysis, including:

• Forward contracts (hedging, speculation)
• Option contracts: long/ short put/ call and strategies in different scenarios.
• Pricing by arbitrage
• Market equilibria
• Choice under uncertanty
• Risk management models: Mean Variance Analysis, CAPM, consumption base CAPM

Largest course at ETH, it covers many crucial concepts in Machine Learning such as:

• Basics (regression vs classification, bias-variance trade-off, model selection, regularization, how to handle imbalanced datasets...)
• Support Vector Machines, Kernels, Polynomical regression...
• Neural Networks: backpropagation, optimizers, gradient descent, CNNs, GANs...
• Dimensionality reduction (PCA, autoencoders...)
• Miscellaneous: K-Means algorithm for clustering, Mixture models, generative models...

First year course, that covers many cool topics such as:

• Definition of thread, bad interleaving, race condition, task graph...
• Different ways to achieve mutual exclusion (locks, atomic operations, transactional memory) and their pros/cons
• Low-level implementation of a lock
• Theoretical perspective: Amdahl's and Gustafson's laws, linearizability, (sequential) consistency, consensus, parallel sorting...
• Message Passing Interface

Lecture I took during my time at Harvard. Topics include:

• Criteria to assess the reproducibility and replicability of a research
• Data Provenance
• Best practices to create a fully reproducible research project
• Computational and statistical tools

Second year course including many topics such as:

• Bit hacks
• Binary representation (two's complements, floating points...)
• Dynamic Memory allocation
• Assembly languages
• Computer Architectures
• Compilers
• Caches
• Memory

Core course. Topics include:

• Image segmentation
• Convolution with famous filters for template matching
• Fourier transform
• Optical flow
• Texture and rendering
• Physically based animation