Machine Learning
This award-winning introductory Machine Learning lecture teaches the foundations of and concepts behind a wide range of common machine learning models. It uses a combination of engaging lectures, challenging mathematical exercises, practically-oriented programming tasks, and insightful tutorials. The lecture was awarded with the TeachInf 2020 award.
Tentative list of topics
- Introduction
- What is machine learning?
- Typical tasks in ML
- k-Nearest neighbors
- kNN for classification and regression
- Distance functions
- Curse of dimensionality
- Decision trees
- Constructing & pruning decision trees
- Basics of information theory
- Probabilistic inference
- Parameter estimation
- Maximum likelihood principle
- Maximum a posteriori
- Full Bayesian approach
- Linear regression
- Linear basis function models
- Overfitting
- Bias-variance tradeoff
- Model selection
- Regularization
- Linear classification
- Perceptron algorithm
- Generative / discriminative models for classification
- Linear discriminant analysis
- Logistic regression
- Optimization
- Gradient-based methods
- Convex optimization
- Stochastic gradient descent
- Deep learning
- Feedforward neural networks
- Backpropagation
- Structured data: CNNs, RNNs
- Training strategies
- Frameworks
- Advanced architectures
- Support vector machines
- Maximum margin classification
- Soft-margin SVM
- Kernel methods
- Kernel trick
- Kernelized linear regression
- Dimensionality reduction
- Principal component analysis
- Singular value decomposition
- Probabilistic PCA
- Matrix factorization
- Autoencoders
- Clustering
- k-means
- Gaussian mixture models
- EM algorithm
Literature
- Pattern Recognition and Machine Learning. Christopher Bishop. Springer-Verlag New York. 2006.
- Machine Learning: A Probabilistic Perspective. Kevin Murphy. MIT Press. 2012
Prerequisites
- Good understanding of Linear Algebra, Analysis, Probability and Statistics.
- Programming experience (preferably in Python).
Schedule
- Due to the current situation, we cannot offer in-person lectures. We will upload videos of lectures and tutorials, and provide pointers to other reference materials. Additionally, we will offer slots for online, live Q&A sessions (timeslots will be provided later).
- Please also note the new lecture period for this semester (02.11.2020 – 12.02.2021)
- All material will be provided via Moodle.
- Discussions will take place via Piazza. Please also use Piazza to ask questions, we won't answer questions sent by email.
- Registration for the course will open soon (most likely in early October).
Organizational details
- Language: English
- Intended audience:
- Master students of the Informatics department (Including Data Engineering & Analytics program).
- Not available for Information Systems (Wirtschaftsinformatik) students.
- Only TUM students are allowed to write the exam.
- Please note that you can only include one of IN2064 / IN2332 in your curriculum
- 8 ECTS
- Grade bonus of 0.3 will be awarded to students who show sufficient work for at least 75% of the homework exercises. Note, that the grades 1.0, 4.3, 4.7 and 5.0 can't be improved.