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Lecture

Introduction to Machine Learning

In course

CH-234: Organic functions and reactions II

To develop basic understanding of the reactivity of aromatic and heteroaromatic compounds. To develop a knowledge of a class of pericyclic reactions. To apply them in the context of the synthesis. ...

Description

This lecture covers the basics of machine learning, including supervised and unsupervised learning, model evaluation, and feature selection. It also delves into advanced topics such as neural networks and deep learning.

Instructors (2)

Nicolai Cramer

Nicolai Cramer was born in Stuttgart, Germany; he studied chemistry at the University of Stuttgart where he graduated in 2003, and earned his PhD in 2005 under the guidance of Professor Sabine Laschat. After a research stage at Osaka University, Japan, he joined the group of Professor Barry M. Trost at Stanford University as a postdoctoral fellow in 2006. From 2007 on, he worked on his habilitation at the ETH Zurich associated to the chair of Professor Erick M. Carreira and recieved the venia legendi in 2010. In 2010, he started as Assistant Professor at the EPF Lausanne and was promoted to Associate Professor in 2013 and to Full Professor in 2015. His main research program encompasses enantioselective metal-catalyzed transformations and their implementation for the synthesis of biologically active molecules. Author profile (Angew. Chem. Int. Ed.) CV

Felix Daniel Bobbink

Official source

Related concepts (23)

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

Unsupervised learning

Unsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.

Feature learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

Self-supervised learning

Self-supervised learning (SSL) is a paradigm in machine learning for processing data of lower quality, rather than improving ultimate outcomes. Self-supervised learning more closely imitates the way humans learn to classify objects. The typical SSL method is based on an artificial neural network or other model such as a decision list. The model learns in two steps. First, the task is solved based on an auxiliary or pretext classification task using pseudo-labels which help to initialize the model parameters.

Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.

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