Menù principale
B024317 - MACHINE LEARNING
Main information
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2019-20
Coorte 2018 - Second Cycle Degree in Computer Engineering
Course year
Second year - First Semester
Belonging Department
Information Engineering (DINFO)
Course Type
Single education field course
Scientific Area
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Credits
9
Teaching Hours
72
Teaching Term
23/09/2019 ⇒ 20/12/2019
Attendance required
No
Type of Evaluation
Final Grade
Course Content
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Course program
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Lectureship
Course Content
- Supervised learning.
- Generalized linear models. Neural networks.
- Support vector machines. Kernels.
- Computational learning theory.
- Unsupervised learning and generative models.
- Probabilistic graphical models.
- Relational learning.
- Neural networks and feature learning.
- Applications.
Suggested readings (Search our library's catalogue)
<h4 class="mt-4">Textbooks</h4>
<dl class="row">
<dt class="col-sm-1">[GBC16]</dt>
<dd class="col-sm-11">I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT
Press, 2016.</dd>
<dt class="col-sm-1">[A18]</dt>
<dd class="col-sm-11">Charu C. Aggarwal
Neural Networks and DeepLearning. Springer,
2018 (freely available from Unifi IP).</dd>
<dt class="col-sm-1">[HTF09]</dt>
<dd class="col-sm-11">T. Hastie, R. Tibshirani, and J. Friedman.
The Elements of Statistical Learning. Data
Mining, Inference, and Prediction. 2nd edition. Springer, 2009.</dd>
<dt class="col-sm-1">[B12]</dt>
<dd class="col-sm-11">D. Barber.
Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.</dd>
</dl>
<h4 class="mt-4">Other texts</h4>
<dl class="row">
<dt class="col-sm-1">[RN10]</dt>
<dd class="col-sm-11">Stuart Russell and Peter Norvig.
Artificial Intelligence: A Modern Approach. 3rd edition. Pearson, 2010.</dd>
<dt class="col-sm-1">[STC00]</dt>
<dd class="col-sm-11">John Shawe-Taylor and Nello Cristianini. Support Vector Machines and other kernel-based learning
methods, Cambridge University Press, 2000.</dd>
</dl>
<dl class="row">
<dt class="col-sm-1">[GBC16]</dt>
<dd class="col-sm-11">I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT
Press, 2016.</dd>
<dt class="col-sm-1">[A18]</dt>
<dd class="col-sm-11">Charu C. Aggarwal
Neural Networks and DeepLearning. Springer,
2018 (freely available from Unifi IP).</dd>
<dt class="col-sm-1">[HTF09]</dt>
<dd class="col-sm-11">T. Hastie, R. Tibshirani, and J. Friedman.
The Elements of Statistical Learning. Data
Mining, Inference, and Prediction. 2nd edition. Springer, 2009.</dd>
<dt class="col-sm-1">[B12]</dt>
<dd class="col-sm-11">D. Barber.
Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.</dd>
</dl>
<h4 class="mt-4">Other texts</h4>
<dl class="row">
<dt class="col-sm-1">[RN10]</dt>
<dd class="col-sm-11">Stuart Russell and Peter Norvig.
Artificial Intelligence: A Modern Approach. 3rd edition. Pearson, 2010.</dd>
<dt class="col-sm-1">[STC00]</dt>
<dd class="col-sm-11">John Shawe-Taylor and Nello Cristianini. Support Vector Machines and other kernel-based learning
methods, Cambridge University Press, 2000.</dd>
</dl>
Learning Objectives
You will learn about several fundamental and some advanced algorithms for statistical learning, you will know the basics of computational learning theory, and will be able to design state-of-the-art solutions to application problems.
Prerequisites
A good knowledge of a programming language, and a solid background in mathematics (calculus, linear algebra, and probability theory) are necessary prerequisites to this course. Previous knowledge of optimization techniques and statistics would be useful but not strictly necessary.
Teaching Methods
Lectures and practical sessions.
Type of Assessment
B024317: 9 CFU
- There is a single oral final exam. You can choose the exam topic but you are strongly advised to discuss with me before you begin working on it.
- Typically, you will be assigned a set of papers to read and will be asked to reproduce some experimental results.
- You will be required to give a short (30 min) presentation during the exam. Please ensure that your presentation includes an introduction to the problem being addressed, a brief review of relevant literature, technical derivation of methods, and, if appropriate, a detailed description of experimental work. You are allowed to use multimedia tools to prepare your presentation. You are responsible for understanding all the relevant concepts and the underlying theory.
- You can work in groups of two to carry out experimental works (three is an exceptional number that you must motivate clearly). If you do so, please ensure that personal contributions to the overall work are clearly identifiable.
B024318: 6 CFU
- Same as above on the first 2/3 of the program and experimental work is optional