Master program in Computer Science. Artificial Intelligence

Transcription

1 Master program in omputer Science Artificial Intelligence

2 AI is taking over the world AI was the star at the latest Google I/O conference 2 hrs of keynote talk almost every aspect of Google products and services relies on AI today

3 AI is taking over the world

4 Big and small players alike are betting on AI

5 New branches from an old tree 14: John von Neuman, ENIA, a giant brain 150: Alan Turing publishes the Turing Test 155: Arthur Samuel, first learning machine (checkers) 15: Dartmouth conference, birth of the term Artificial Intelligence 158: Frank Rosenblatt, Perceptron, artificial neural network 13: J. Alan Robinson implements general deduction on a computer 1: Joseph Weizenbaum, ELIZA -- the first chatbot 1: Marvin Minsky, Seymour Papert. Fundamental limits of Perceptron 172: Alain olmerauer, Prolog -- efficient computing by rules : AI Winter. NN approaches discredited. Symbolic approaches do not deliver. 180: Expert systems (rules+knowledge) deployed in a number of applications 185: NNs rediscovered; backpropagation with hidden layers 10: NNs out of fashion (hard limits on data and computing) 15: Principled (statistical) approaches to Machine Learning 17: IBM s Deep Blue beats Garry Kasparov at chess (brute force) 2015: Microsoft s deep rectified model exceeds human accuracy in classifying images 201: Google s AlphaGo beats Lee Sedol at Go

6 areer opportunities In established companies that are building the next generation of intelligence and language understanding for their products, for example: intelligent personal assistants opinion mining systems customer support system biomedical applications computer games smart adaptive devices robots smart planning systems In your own startup, a chance to create new product categories In consulting for companies or public bodies In Research and Academy, working on advancing fundamental theories and applications alike significant amounts of venture capital available unexplored market opportunities acquisition, merge, hiring, or growth significant lack of in-house AI expertise AI will have a transformative impact on any type of business setting public policies, informing decision-making In legal, economics, ethics, arts, humanities think at the impact of mobile. It s like that.

7 Second year omputational Semester 2 Human language Semester 3 Parallel and distributed systems: paradigms and and data analysis Free choice Intelligent Systems for pattern recognition technologies mathematics for learning Machine learning

8 Second year Intelligent Systems for and data analysis Machine learning pattern recognition mathematics for learning Human language Semester 3 approaches Symbolic to AI Search, exploration, planning technologies onstraint satisfaction systems Uncertain and probabilistic reasoning Parallel and distributed non-standard logics systems: paradigms and Free choice Semantic networks and description logics Rules systems and their efficient implementation omputational lassical AI Semester 2

9 Second year omputational Semester 2 Human language Mathematical concepts and tools for AI Parallel and distributed systems: paradigms and and data analysis Intelligent Systems for pattern recognition Semester 3 technologies mathematics for learning Machine learning Free choiceand optimization Numerical analysis Statistics, approximation, fitting Hands-on sessions students will apply and test techniques and algorithms in lab setting MATLAB and other software tools

10 Second year omputational mathematics for learning and data analysis Machine learning Machines that learn Semester 2 Human language Semester 3 and paradigms Principles in learning from technologies data Building adaptive intelligent systems Developing predictive Parallel and distributed and Neural networks, in several flavours systems: paradigms Free choice Probabilistic Support Vector Machines and kernel-based Intelligent Systems for learning theory and model Statistical pattern recognition validation

11 Machines Second yearthat read, write, listen and speak omputational Semester 2 Human language Semester 3 technologies Parallel and distributed mathematics for learning systems: paradigms and and data analysis Machine learning Intelligent Systems for pattern recognition Principles, and state-of-the-art in natural language analysis Statistical ML, deep learning morphology, NL essentials: tokenization, POS-tagging, parsing, etc. choice Semantics: lexical, Free distributional Applications: entity recognition, linking, classification, summarization, opinion mining, sentiment analysis Question answering, language inference, dialogic interfaces, machine translation NLP libraries: NLP, Theano, Tensorflow

12 Second year Semester 2 Human language Semester 3 technologies omputational Parallel and distributed mathematics for learning systems: paradigms and and data analysis Machine learning Intelligent Systems for Free choice How to build and program those intelligent machines pattern recognition parallel and distributed architectures latency, service time, speedup, scalability for parallel and distributed programming design patterns Fastflow, optimizations

13 Sentient AI: patterns, signal, and image processing omputational mathematics for learning and data analysis Machine learning Second year Signal processing and time series Semester Image2 processing, visual feature detectors Semester 3 Learning for machine vision Human for non-vectorial language Neural networks data technologies physiological data, sensor streams, etc. Kernel and adaptive methods for relational data Applications Parallel and distributed machine vision, bio-informatics, robotics, medical imaging systems: paradigms and Free choice libraries and tools Intelligent Systems for pattern recognition

14 Second year Putting it all together Semester 3 Human language project Teamwork, jointtechnologies design, development and testing of a application omputationalcomplex AI-based Parallel and distributed sensors, IoT mathematics for learning systems: paradigms and mobile and data analysis natural interface cloud-based AI Machine learning Intelligent Systems for End result: a user-ready patternintelligent recognition application featuring interaction and behaviour Free choice apstone Semester 2

15 Second year s FU engineering Semester Algorithm (KD) 2 Data mining (KD) language Mobile and Human cyber-physical systems (IT) Information retrieval (KD) omputational neuroscience (ENG) omputational Parallel and distributed Social and ethical issues in computer mathematics for learning systems: paradigms and technology and data analysis Robotics Semantic web Free choice Intelligent Systems for pattern recognition technologies s FU Machine learning Semester 3

16 People Maria Simi Alessio Micheli Davide Bacciu Marco Danelutto Antonio Frangioni & Federico Poloni Giuseppe Attardi Vincenzo Gervasi