Machine Learning: Pattern Recognition
Here are the lecture notes
I teach the Machine Learning: Pattern Recognition master's course at the UvA. This is a challenging course for master's students, which provides a solid introduction to Machine Learning and Pattern Recognition, with a focus on probabilistic modelling. The book of the course is Chris Bishop's Pattern Recognition and Machine Learning, and we cover pretty much all of the topics in the book with this course, with occasional side-excursions to cover additional material. I also wrote some additional lecture notes to spend some more time on the more basic aspects of the course. You are encouraged to use a recent version of acrobat reader to read those, as the PDF contains animations which some PDF readers cannot render.
The course consists of a weekly two-hour lecture, a two-hour exercise session and a two-hour lab. The exercise sessions follow the lectures and reinforce the material by providing exercises that prove or illustrate items seen in the preceding lecture. The computer labs provide more hands-on interaction with the material. Examples of labs include: implementing logistic regression, the E.M. algorithm for mixtures of Gaussians, an email spam filter, face recognition using principal component analysis, etc.
Prof. Dariu Gavrila provides two guest lectures where the application of pedestrian detection from a moving vehicle is used to highlight such issues as feature selection, very high accuracy recognition, dealing with very large datasets, real-time constraints, etc.
Here are the slides to the lectures:
- Lecture 1: Introduction
- Introductory material and practical stuff.
- Lecture 2: Training and Testing
- This covers overfitting and generalisation, regularisation, etc.
- Lecture 3: Linear discriminants
- Simple classifiers
- Lecture 4: Bayesian decision theory
- Probabilistic modelling, and making decisions based on probabilities.
- Lecture 5: Graphical Models
- Bayesian Networks, Markov Random Fields and algorithms for efficient inference with these models.
- Lecture 6: Expectation Maximisation
- Learning in Graphical Models
- Lecture 7: Sequential Data
- Hidden Markov Models and Kalman Filters (but no CRFs, for now)
- Lecture 8: Neural Networks
- Multilayer Perceptrons, and techniques to train them
- Lecture 9: Kernel Methods
- Gaussian Processes and Support Vector Machines
- Lecture 10: Dimensionality Reduction
- Linear and non-linear dimensionality reduction: Principal Component Analysis, Factor analysis, autoencoders, locally linear embedding, etc.
- Lecture 11: Sampling
- Basic sampling algorithms and Markov-chain Monte Carlo.
- Lecture 12: Combining Models
- Bagging, boosting, etc.
Student supervision
Ph.D. Students
I am supervising the following PhD students at the moment:
- Camille Sallaberry
- Luc Schoot Uiterkamp
The Ph.D. students who are now doctors
- Sara Falcone
- Jered Vroon
- Ahmed Nait Aicha
- Ninghang Hu
- Athanasios "Thanasis" Noulas
- Tim van Kasteren
- Julian Kooij
M.Sc. Students
MSc students that I am currently supervising
- M. Dethmers
- T. Hoppe
- D. Vinke
- J. van Geel
- C. Bourbon de Parme
- A. Günev
- T. Bianchi
- S. de Graaf
- D. Lammers
- C. Asbreuk
Students that whose M.Sc. projects I have supervised in the past
- A. Sabermanesh
- Blink and You Miss It: Exploring Event-Related Blinks in a Purpose-Designed Simulated High-Performance Flight Environment
- M.B. Bîndilă
- Grounding Detailed Symbolic Descriptions in Paintings: A Novel Dataset and Embedding Approach
- Ho Tak Fong
- Sensor-Based 3D Reconstruction of Pillow Deformation for Shoulder Surgery Recovery
- N. Kezins
- Selective Knowledge Transfer via communication-aware Model Alignment
- R.A. Gaibar
- Decoding Emotion in Motion : Affective Communication Through Wheeled Telepresence Robot Movement
- S. Onrust
- The influence of the perception of social touch on human-robot interaction
- V. Krishnaswamynathan
- Decoding Neural Signatures of Language Comprehension and Production through EEG based Brain-Computer Interfaces
- E. Mytaros
- Enhancing socially aware navigation of robots in healthcare environments through predicting human trajectories
- O. Solovyeva
- How Hard Can It Prompt? Adventures in Cross-model Prompt Transferability
- L.P.W. van den Heuvel
- Diffuse more objects with fewer labels
- A. Bachir Kaddis Beshay
- Cold-start Active Learning for Text Classification of Business Documents
- M.W. Muller
- SPaS: Sparse Parameterized Shortcut Connections for Dynamic Sparse-to-Sparse Training
- Y. Jia
- Exploring Emotional Transmission Through Haptics in Mediated Social Interaction : Transmit happiness through haptic hugs
- J. Koning
- Reducing loneliness in seniors using an automated calling system for activity invitation
- M. Bui
- Optimal care for geriatric hip fracture patients : an interdisciplinary perspective on preoperative decision-making and postoperative rehabilitation
- A.-M. Jutte
- The smart annotation tool : optimizing semi-automated behavioural annotation using an AutoML framework supported by classification correctness prediction
- A. A. Singhal
- Improving extreme multi-label text classification with sentence level prediction
- T.J.J. Wittendorp
- Pig localization using computer vision
- N.D. Deshpande
- Machine learning techniques for the analysis of affective components of sign language
- D. Gulhane
- What Face Would You Like To Have? : The Effects of an Avatar's Facial Features on Social Presence
- S.D. Meijer
- The implementation of LiDAR-based traffic detection and tracking
- L. Schoot Uiterkamp
- Classification of Empathy and Call for Empathy in Child Help Forum Messages
- K.J.W. te Voortwis
- A picture is worth 1000 words : Introducing the visual modality to the query dependent video clip selection process
- R. Ma
- Anomaly detection for Linux system log
- G.D. Nayanar
- Autonomy, AI Perception and Safety : a Safety Evaluation Framework for AI Perception Models Used In Agricultural Autonomous Vehicles
- M.W. van Veen
- Normal map prediction from light field data through deep learning
- R. Fasel
- Adapting the variational auto encoder for datasets with large amounts of missing values.
- A. Sadananda Bhat
- Hierarchical deep neural networks for MeSH subject prediction
- D.R. de Meij
- Predicting blood glucose for type 2 diabetes patients
- S. van Waveren
- Automatic image caption generation for digital cultural emages collections
- R. Knuppe
- Drift correction using a multi-rate extended Kalman filter
- J. Kleine Deters
- Therapeutic exercise assessment automation, a hidden Markov model approach.
- X. Jia
- Understanding social signals from nonverbal behaviors in a mobile setting
- K.K. Thirukokaranam Chandrasekar
- Tracking and Control of Soft, Self-Folding Miniaturized Agent using Ultrasound Images
- Nourolhoda Alemi
- Hoda investigated how active learning can be applied to object detection, when very large datasets of unlabelled images are available, but labelling is labour-intensive
- Cem Özalp
- Cem worked on recognising emotions in (non-violent) interpersonal touch, as measured by pressure sensors. Volunteers touch each other's lower arm, with the touch conveying different emotions.
- Kirstin Rieping (together with Ben Kröse)
- Kirstin investigated how feature extraction and unsupervised models can be combined.
- Tjeerd van Dijk
- Gesture Recognition using a Time-of-Flight Camera and Hidden Markov Models
- Jeroen Kools
- Recognizing Non-verbal Social Behavior with Accelerometers
- Wouter Josemans
- fall detection of elderly people with combined 2D and 3D cameras
- Bram Stoeller
- Recognizing individuals by appearance across non-overlapping stereo cameras
- Domenic Vossen
- Detecting Speaker Status in a Social Setting with a Single Triaxial Accelerometer
- Silvia-Laura Pintea
- Orientation Estimation from ceiling-mounted cameras
- Nimrod Raiman
- Move and I will tell you who you are: detecting deceptive roles in low-quality [video] data
- Yanxia Zhang
- Multi-people Tracking using Graph Representation with Ceiling-Mounted Video Cameras