Ecole Polytechnique Fédérale de Lausanne :


  • EPFL : Ecole Polytechnique F√©d√©rale de Lausanne
  • EPFL : Ecole Polytechnique F√©d√©rale de Lausanne

EPFL CDM IF
ODY 3 18 (Odyssea)
Station 5
CH - 1015 Lausanne

Tel : (+41) 21 693 01 22
Fax : (+41) 21 693 00 20

2010/2011 - Finance && Computer Sciences courses

  • CS-444: Virtual reality
    Learning outcomes: This course introduces the key concepts and technologies of immersive 3D real-time interaction mostly acknowledged as Virtual Reality. The goal of VR is to embed the users in a potentially complex virtual environment while ensuring that they are able to react as if this environment were real, even if it is not realistic (in the sense of CG special effects for film productions). The course will be illustrated with application-oriented case studies such as Virtual Prototyping, Rehabilitation, Training etc. After attending the course the student should master critical concepts such as presence or flow and be able to identify where computing resource should be allocated to maintain an intuitive, transparent, and involving 3D interaction.

    Content:
    Basic concepts of Virtual reality:
    - Human perception and action abilities
    - Immersion and Presence
    Interfaces:
    - Sensors and actuators
    - 3D projection and interaction techniques
    Software platforms:
    - Visual, audio and haptic rendering


  • - : Navigation Techniques
    Learning outcomes: To learn the modelling of the behaviour of various sensors used in navigation to develop algorithms for the estimation of parameters in real-time To grasp the importance of acquiring position data together with other environmental data.

    Content:
    Navigation instruments:
    - Satellite techniques
    - Inertial techniques: gyroscopes and accelerometers
    - Integrated sensors
    Navigation software:
    - Sequential least squares
    - Prediction, filtering and smoothing
    - Movement modelling
    - Noise modelling and propagation
    - Bayes and Kalman filters
    - Correlated observations
    - Oganization of the computations
    Application domains:
    - GPS positioning
    - GPS/INS integration
    - Remote sensing
    - Air traffic control
    - Robotics


  • CS-433: Pattern classification and machine learning
    Learning outcomes: Data classification is at the heart of automatized learning. In this course, the student will learn to master relevant classification algorithms (artificial neural networks, Bayes classification, support vector machine, expectation maximization), and understand their basic theoretical background.

    Content:
    Classification and supervised learning:
    - The problem of automatic classification
    Artificial Neural Networks:
    - Simple perceptrons and linear separability
    - Multilayer Perceptrons: Backpropagation Algorithm
    - The problem of generalization
    Optimal decision boundary and density estimation:
    - Maximum Likelihood and Bayes
    - Mixture Models, expectation maximization (EM)
    Unsupervised learning:
    - Principal components analysis
    - Clustering, K-means


  • Research Engineer: OZWE - Rolex Learning Center
    Object : Based on on-going work at CRAFT, this project will investigate the machine learning challenges of recognizing book titles and authors among a book. Makes extensive usage of the OAI-PMH BNF warehouse. Data mining, artificial intelligence techniques and machine learning with Frederic Kaplan.


  • FIN-401: Introduction to finance
    Programme :
    - Introduction to finance
    - Arbitrage, discounting, and the term structure of interest rates
    - Introduction to the valuation of bonds and stocks
    - Risk and return
    - Capital Budgeting
    - Capital Structure Decisions
    - Financial derivatives
    - Option Valuation

    Key words : Cases in finance - Credit risk and fixed income analysis - Derivatives - Financial econometrics - Investments - Real options and financial structuring - Design of market-based solutions to allocation problems - Private equity.


  • COM-504: Stochastic calculus I
    Objectives : To get a deep understanding of the fundamental notions of stochastic calculus necessary for financial applications such as option pricing and hedging.

    Content : Probability review :
    - probability spaces, sigma-fields, random variables
    - probability measures, distributions, independence, expectation
    - inequalities, convergences, limit theorems
    - conditional expectation Discrete-time processes :
    - random walks, filtrations, martingales
    - Doob's theorems, martingale transforms
    - Markov processes, Gaussian vectors Continuous-time processes
    - Brownian motion, Gaussian processes, Kolmogorov's theorem
    - martingales, Levy's theorem, Doob's theorems
    - processes with bounded variation, quadratic variation Stochastic calculus
    - Riemann-Stieltjes integral, Ito's integral
    - Stratonovic's integral, quadratic variation of Ito's integral
    - Ito's formula, Ito's processes, Stratonovic's formula
    - first approach of stochastic differential equations



  • HEC : Mathematics of Compound Interest
    Objectives : Provide the students with the understanding of fundamental concepts of financial mathematics applied in calculating present and accumulated values for various streams of cash flows.

    Content :
    - Interest rates
    - Valuation of annuities
    - Loan repayment
    - Internal rate of return
    - Bond valuation
    - Term structure of interest rates
    - Cash flow duration and immunization
    - Additional topics



  • MGT-466: Negotiation techniques
    Objectives : To learn and practice the negotiating process and negotiation theory

    Content :
    - Defining negotiation and the process
    - Frames, goals and strategies
    - Planning a negotiation
    - conditional expectation
    - The impact of cultural difference on the process of negotiation
    - Trust and truth in negotiations
    - Team role-plays, Role play, role play, role play... with feedback



  • COM-418: IT security engineering
    Objectives : The objective of this course is to provide with a sound basis in IT security and privacy principles, technologies, standards, and best practices, including terminology, taxonomies of problems and solutions, methodologies for recognizing and fending off intrusions, techniques for securing hardware, software and information at rest and in transit, as well as common tools for building secure systems and ensuring their compliance with established regulations.

    Content :
    Basic Principles :
    - Problem statement and taxonomy of threats
    - Solution elements and taxonomy of defenses
    - Security engineering principles
    Technologies :
    - Identity 2.0 and multi-factor authentication
    - Data classification and leakage protection
    - Authorization and access control policies
    - Hardware platform security
    - Operating system security
    - Database security
    - Application security
    - Privacy
    Engineering for security :
    - Faults, errors, and failures
    - Vulnerabilities and attack vectors
    - Intrusion prevention, detection, and recovery
    - Vulnerability scanning and penetration testing
    - Elements of digital forensics
    Standards and best practices in security governance :
    - Quality assurance
    - Audit and compliance



  • Research Engineer: Text-Mining CRAFT - Rolex Learning Center
    Object : Based on on-going work at CRAFT, this project will investigate the machine learning challenges of recognizing book titles and authors among a book. Data mining, artificial intelligence techniques and machine learning with Frederic Kaplan.