Update teaching.md

Author: kmaqua

teaching.md

@@ -5,14 +5,6 @@ description: Teaching
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-### Machine Learning (Year 3, MSc)
-The aim of this course is to introduce students to the theory and practice of modern machine learning methods. Extracting information from the unprecedented amount of data (aka. big data) that has been collected in recent years is a very important task in science and engineering, with great social and economical impact. Machine learning addresses the problem of how computers can learn and extract information automatically from data, and it is behind many methods used in artificial intelligence, data mining or adaptive system design. It is widely applied in practice in most disciplines where data is available, including, e.g., electrical engineering, computer science, or medicine. Students will learn the main concepts from theory and practice of how to convert observations given in the form of data into expertise. You will learn how to model learning and inference problems and how to design and analyse algorithms to solve them. The module will also introduce popular machine learning algorithms.
-
-Upon completion of this module, the student will be able to demonstrate and apply knowledge and understanding of:
-- Standard machine learning tasks scenarios and the general methodology to learn from data under various conditions.
-- Basic concepts and ideas, as well as the theory underlying machine learning problems and algorithms.
-- Several popular modern machine learning algorithms, with standard tools and techniques used in their design and analysis, and will be able to evaluate and analyse them.
-- How to approach real world machine learning problems, how to model problems, pre-process data, as well as design, select and implement appropriate learning algorithms.
 
 
 ### Deep Learning (Year 3, MSc)
@@ -41,4 +33,13 @@ The learnt topics are illustrated with applications: face recognition, or machin
 - Develop insight into the problems involved in applying a variety of computer vision and pattern recognition techniques to deal with practical scenarios
 - Understand and apply the concepts of visual geometry in selected computer vision applications
 - Analyse and compare the strengths and weaknesses of popular approaches
-- Implement various algorithms in a range of CVPR applications through specific programming environments (Matlab, python)   
+- Implement various algorithms in a range of CVPR applications through specific programming environments (Matlab, python)
+
+  ### Machine Learning (Year 3, MSc)
+The aim of this course is to introduce students to the theory and practice of modern machine learning methods. Extracting information from the unprecedented amount of data (aka. big data) that has been collected in recent years is a very important task in science and engineering, with great social and economical impact. Machine learning addresses the problem of how computers can learn and extract information automatically from data, and it is behind many methods used in artificial intelligence, data mining or adaptive system design. It is widely applied in practice in most disciplines where data is available, including, e.g., electrical engineering, computer science, or medicine. Students will learn the main concepts from theory and practice of how to convert observations given in the form of data into expertise. You will learn how to model learning and inference problems and how to design and analyse algorithms to solve them. The module will also introduce popular machine learning algorithms.
+
+Upon completion of this module, the student will be able to demonstrate and apply knowledge and understanding of:
+- Standard machine learning tasks scenarios and the general methodology to learn from data under various conditions.
+- Basic concepts and ideas, as well as the theory underlying machine learning problems and algorithms.
+- Several popular modern machine learning algorithms, with standard tools and techniques used in their design and analysis, and will be able to evaluate and analyse them.
+- How to approach real world machine learning problems, how to model problems, pre-process data, as well as design, select and implement appropriate learning algorithms.