General Info: M.Sc. in Data Sciences and Business Analytics, ESSEC Business School and CentraleSupélec, Fall 2017
Lecture hours: Tuesday, 13:15 - 16:15
Lecture room: ESSEC campus, La Defense
Instructor: Fragkiskos Malliaros
Email: fragkiskos.me [at] gmail.com
Office hours: Right after class (or send me an email and we will find a good time to meet)
Machine learning is the scientific field that provides computers the ability to learn without being explicitly programmed (definition by Wikipedia). Machine learning lies at the heart of many real-world applications, including recommender systems, web search, computer vision, autonomous cars and automatic language translation.
The course will provide an overview of fundamental topics as well as important trends in machine learning, including algorithms for supervised and unsupervised learning, dimensionality reduction methods and their applications. A substantial lab section will involve group projects on a data science competition and will provide the students the ability to apply the course theory to real-world problems.
Schedule and Lectures
The topics of the lectures are subject to change (the following schedule outlines the topics that will be covered in the course). The slides for each lecture will be posted in piazza
just before the start of the class. The due dates of the assignments/project are subject to change.
|1||October 4||Introduction||Lecture 1||
|2||October 10||Dimensionality reduction||Lecture 2||
|3||October 17||Model selection and evaluation||Lecture 3||Assignment 1 out
|4||October 24||Linear and logistic regression||Lecture 4||Assignment 2 outProject proposal due on October 29
|5||October 31||Probabilistic classifiers and linear discriminant analysis||Lecture 5||Assignment 1 due on November 5
|6||November 7||Non-parametric learning and nearest neighbor methods||Lecture 6||
|7||November 14||Tree-based methods and ensemble learning||Lecture 7||
|8||November 21||Support Vector Machines||Lecture 8||Assignment 2 due on November 26
|9||November 28||Unsupervised learning: clustering||Lecture 9||
|10||December 12||Project presentations||Project final report due
[October 4] Lecture 1: Introduction
Introduction to machine learning, administrivia, course structure and overview of the topics that will be covered in the course. Bacic concepts in optimization.
[October 10] Lecture 2: Dimensionality reduction
Dimemensionality reduction techniques. Singular Value Decomposition (SVD). Principal Component Analysis (PCA). Multidimensional Scaling (MDS) and nonlinear dimensionality reduction.
- SVD and Low Rank Matrix Approximations, lecture notes by Tim Roughgarden and Gregory Valiant (Stanford University)
- Understanding Machine Learning: From Theory to Algorithms (Section 23.1)
- J. B. Tenenbaum, V. De Silva, and J. C. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 290:5500, pp. 2319-2323, 2000
- S. T. Roweis and L. K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 290:5500, pp. 2323-2326, 2000
- M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, 2001
[October 17] Lecture 3: Model selection and evaluation
Overfitting and generalization. Bias-variance tradeoff. Training, validation and test sets. Cross-validation. Evalution of supervised learning algorithms.
- M. Kuhn and K. Johnson. An Introduction to Feature Selection. Applied Predictive Modeling, pages 487-519, 2013. [For the first part of the lecture on feature selection].
- Model evaluation, model selection, and algorithm selection in machine learning: Part I (Basics), Part II (Bootstrapping and uncertainties), and Part III (Cross-validation and hyperparameter tuning). Interesting blog post by Sebastian Raschka, 2016.
[October 24] Lecture 4: Linear and logistic regression
Supervised learning models. Linear regression. Linear classification models. Logistic regression. Maximum likelihood estimation.
[October 31] Lecture 5: Probabilistic classifiers and linear discriminant analysis
Bayes rule. Naive Bayes classifier. Maximum a posterioti estimation. Linear discriminant analysis (LDA).
[November 7] Lecture 6: Non-parametric learning and nearest neighbor methods
Introduction to non-parametric learning methods. Distance and similarity metrics. Nearest neighbor algorithms.
[November 14] Lecture 7: Tree-based methods and ensemble learning
Decision trees. Ensemble learning. Bagging and Boosting. The AdaBoost algorithm.
[November 21] Lecture 8: Support Vector Machines
Maximum margin classifier. Linear SVMs. Primal and dual optimization problems. Non-linearly separable data and the kernel trick. Regularization and the non-separable case.
[November 28] Lecture 9: Unsupervised learning: clustering
Introduction to unsupervised learning methods. Data clustering. Hierarchical clustering. k-means clustering. Spectral clustering.
Lecture 10: Project presentations
Poster (or oral) presentation of the project of each time.
Course Structure and Objectives
Each section of the course is divided into 1h30' lecture and 1h30' lab. The labs will include hands-on assignments (using Python) and will provide the students the opportunity to deal with ML tasks in practice.
The course aims to introduce students to the field of machine learning by:
- Covering a wide range of topics, methodologies and related applications.
- Giving the students the opportunity to obtain hands-on experience on dealing with.
We expect that by the end of the course, the students will be able to:
- Identify problems that can be solved using machine learning methodologies.
- Given a problem, identify and apply the most appropriate algorithm(s).
- Implement some of those algorithms from scratch.
- Evaluate and compare machine learning algorithms for a
- Deal with real-world data challenges.
There is no official prerequisite for this course. However, the students are expected to:
- Have basic knowledge of probability theory and linear algebra.
- Be familiar with at least one programming language (e.g., Python or any language of their preference).
There is no single requiered textbook for the course. We will recommend specific chapters from the following books:
- Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.
- Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2011.
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition, Springer, 2017.
- Jure Leskovec, Anand Rajaraman, and Jeff Ullman. Mining of Massive Datasets. Cambridge University Press, 2014.
The evaluation of the course will be based on the following:
- Two assignments: the assignments will include theoretical questions as well hands-on practical questions and will familiarize the students with basic graph mining and analysis tasks.
- Project: The students are expected to form groups of 3-4 people, propose a topic for their project, and submit a final project report (it would also be interesting to organize a poster session at the end of the quarter). Please, read the project section for more details.
The grading will be as follows:
| Assignment 1 (individually): || 20%
| Assignment 2 (groups of 3-4 students): || 35%
|| Project (groups of 3-4 students): || 45%
All of your work must be your own. Don't copy another student's assignment, in part or in total, and submit it as your own work. Acknowledge and cite source material in your papers or assignments.
Details about the project of the course will be posted soon on piazza.
Please find below a list of conferences related to the contents of the course (mostly in the field of machine learning and data mining. We provide the DBLP website of each venue where you can access the proceedings (papers, tutorials, etc).
Check out the website of each conference (e.g., KDD 2016
) for more information.